{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fb9b96b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T09:52:57.146766Z",
     "iopub.status.busy": "2024-05-12T09:52:57.146000Z",
     "iopub.status.idle": "2024-05-12T09:53:14.450271Z",
     "shell.execute_reply": "2024-05-12T09:53:14.449378Z"
    },
    "papermill": {
     "duration": 17.31143,
     "end_time": "2024-05-12T09:53:14.452350",
     "exception": false,
     "start_time": "2024-05-12T09:52:57.140920",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-12 09:53:05.461417: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-05-12 09:53:05.461511: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-05-12 09:53:05.591647: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start on cuda:0 device.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import torch\n",
    "import torchvision\n",
    "from PIL import Image\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torchvision import models, transforms\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(\"start on {} device.\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0cccdd6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T09:53:14.460480Z",
     "iopub.status.busy": "2024-05-12T09:53:14.459951Z",
     "iopub.status.idle": "2024-05-12T09:53:14.467251Z",
     "shell.execute_reply": "2024-05-12T09:53:14.466562Z"
    },
    "papermill": {
     "duration": 0.013155,
     "end_time": "2024-05-12T09:53:14.469083",
     "exception": false,
     "start_time": "2024-05-12T09:53:14.455928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 保存训练模型名称\n",
    "save_model_name = \"vgg16_base_cifar10\"\n",
    "# 训练的轮数\n",
    "epoch = 50\n",
    "# 一批数据大小\n",
    "batch_size = 128\n",
    "# 丢弃率\n",
    "dropout = 0.5\n",
    "# 数据集路径\n",
    "# /kaggle/input/dogs-vs-cats\n",
    "dataset_path = \"./dataset\"\n",
    "# tensorboard路径\n",
    "writer = SummaryWriter(\"./logs\")\n",
    "# 学习率\n",
    "# learning_rate = 0.01\n",
    "# 1e-2=1 x (10)^(-2) = 1 /100 = 0.01\n",
    "learning_rate = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e8b5606",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T09:53:14.476422Z",
     "iopub.status.busy": "2024-05-12T09:53:14.476147Z",
     "iopub.status.idle": "2024-05-12T09:53:21.028474Z",
     "shell.execute_reply": "2024-05-12T09:53:21.027569Z"
    },
    "papermill": {
     "duration": 6.558342,
     "end_time": "2024-05-12T09:53:21.030567",
     "exception": false,
     "start_time": "2024-05-12T09:53:14.472225",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./dataset/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 170498071/170498071 [00:02<00:00, 59097825.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./dataset/cifar-10-python.tar.gz to ./dataset\n",
      "Files already downloaded and verified\n",
      "训练数据集长度: 50000\n",
      "测试数据集长度: 10000\n",
      "torch.Size([128, 3, 32, 32])\n",
      "torch.Size([128, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.ToTensor()\n",
    "train_data = torchvision.datasets.CIFAR10(dataset_path, train=True, download=True,\n",
    "                                          transform=transform)\n",
    "train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=False)\n",
    "\n",
    "test_data = torchvision.datasets.CIFAR10(dataset_path, train=False, download=True,\n",
    "                                         transform=transform)\n",
    "test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=False)\n",
    "\n",
    "train_data_size = len(train_data)\n",
    "test_data_size = len(test_data)\n",
    "print(\"训练数据集长度: {}\".format(train_data_size))\n",
    "print(\"测试数据集长度: {}\".format(test_data_size))\n",
    "\n",
    "for data in train_dataloader:\n",
    "    imgs, targets = data\n",
    "    print(imgs.shape)\n",
    "    break\n",
    "\n",
    "for data in test_dataloader:\n",
    "    imgs, targets = data\n",
    "    print(imgs.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "360a2896",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T09:53:21.043891Z",
     "iopub.status.busy": "2024-05-12T09:53:21.043560Z",
     "iopub.status.idle": "2024-05-12T09:53:21.059649Z",
     "shell.execute_reply": "2024-05-12T09:53:21.058838Z"
    },
    "papermill": {
     "duration": 0.024858,
     "end_time": "2024-05-12T09:53:21.061541",
     "exception": false,
     "start_time": "2024-05-12T09:53:21.036683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class VGG16_BASE(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(VGG16_BASE, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            # vgg16原本时 nn.AdaptiveAvgPool2d((7, 7))，由于输入图片大小导致\n",
    "            # nn.AvgPool2d(kernel_size=1, stride=1)\n",
    "        )\n",
    "        self.classifier = nn.Sequential(\n",
    "            # 原始模型vgg16输入image大小是224 x 224\n",
    "            # CIFAR10输入image大小是32 x 32\n",
    "            # 大小是小了 7 x 7倍\n",
    "            nn.Linear(512, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = self.features(x)\n",
    "        # output = output.view(output.size(0), -1)\n",
    "        output = torch.flatten(output, 1)\n",
    "        output = self.classifier(output)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f94465a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T09:53:21.074590Z",
     "iopub.status.busy": "2024-05-12T09:53:21.074322Z",
     "iopub.status.idle": "2024-05-12T09:53:21.586017Z",
     "shell.execute_reply": "2024-05-12T09:53:21.585017Z"
    },
    "papermill": {
     "duration": 0.520479,
     "end_time": "2024-05-12T09:53:21.588090",
     "exception": false,
     "start_time": "2024-05-12T09:53:21.067611",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG16_BASE(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (12): ReLU(inplace=True)\n",
      "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (16): ReLU(inplace=True)\n",
      "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (19): ReLU(inplace=True)\n",
      "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (26): ReLU(inplace=True)\n",
      "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (32): ReLU(inplace=True)\n",
      "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (36): ReLU(inplace=True)\n",
      "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (39): ReLU(inplace=True)\n",
      "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (42): ReLU(inplace=True)\n",
      "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=512, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 网络模型相关初始化\n",
    "# 创建网络模型\n",
    "mymod = VGG16_BASE()\n",
    "print(mymod)\n",
    "\n",
    "mymod = mymod.to(device)\n",
    "# 损失函数\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "# loss_fn = loss_fn.to(device)\n",
    "\n",
    "# 优化器\n",
    "optimizer = torch.optim.SGD(mymod.parameters(), lr=learning_rate, weight_decay=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ce18b9e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T09:53:21.601859Z",
     "iopub.status.busy": "2024-05-12T09:53:21.601548Z",
     "iopub.status.idle": "2024-05-12T10:07:07.731244Z",
     "shell.execute_reply": "2024-05-12T10:07:07.730387Z"
    },
    "papermill": {
     "duration": 826.13896,
     "end_time": "2024-05-12T10:07:07.733235",
     "exception": false,
     "start_time": "2024-05-12T09:53:21.594275",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start train.\n",
      "第0轮训练用时：15.309627532958984 s\n",
      "第0轮训练loss：1.5329919334793092\n",
      "第0轮训练精度：0.42294\n",
      "------\n",
      "第0轮测试用时：2.3563270568847656 s\n",
      "第0轮测试loss：1.2846131616592407\n",
      "第0轮测试精度：0.5406\n",
      "------\n",
      "第1轮训练用时：14.474034309387207 s\n",
      "第1轮训练loss：0.9999765614318847\n",
      "第1轮训练精度：0.6416\n",
      "------\n",
      "第1轮测试用时：2.342064142227173 s\n",
      "第1轮测试loss：1.0631435230255126\n",
      "第1轮测试精度：0.6279\n",
      "------\n",
      "第2轮训练用时：14.262362718582153 s\n",
      "第2轮训练loss：0.7461644832992553\n",
      "第2轮训练精度：0.73672\n",
      "------\n",
      "第2轮测试用时：2.427743434906006 s\n",
      "第2轮测试loss：1.0587359626770019\n",
      "第2轮测试精度：0.6592\n",
      "------\n",
      "第3轮训练用时：14.292752027511597 s\n",
      "第3轮训练loss：0.567556633014679\n",
      "第3轮训练精度：0.80342\n",
      "------\n",
      "第3轮测试用时：2.2722983360290527 s\n",
      "第3轮测试loss：1.237079986190796\n",
      "第3轮测试精度：0.6205\n",
      "------\n",
      "第4轮训练用时：14.200172185897827 s\n",
      "第4轮训练loss：0.4312685500526428\n",
      "第4轮训练精度：0.85212\n",
      "------\n",
      "第4轮测试用时：2.291776418685913 s\n",
      "第4轮测试loss：0.9334003086090088\n",
      "第4轮测试精度：0.7103\n",
      "------\n",
      "第5轮训练用时：14.24055790901184 s\n",
      "第5轮训练loss：0.3214719367313385\n",
      "第5轮训练精度：0.89022\n",
      "------\n",
      "第5轮测试用时：2.320784091949463 s\n",
      "第5轮测试loss：0.7692245864868164\n",
      "第5轮测试精度：0.7672\n",
      "------\n",
      "第6轮训练用时：14.199138879776001 s\n",
      "第6轮训练loss：0.23437394525527955\n",
      "第6轮训练精度：0.91948\n",
      "------\n",
      "第6轮测试用时：2.2753655910491943 s\n",
      "第6轮测试loss：0.9576991064071655\n",
      "第6轮测试精度：0.7378\n",
      "------\n",
      "第7轮训练用时：14.265502691268921 s\n",
      "第7轮训练loss：0.17647030292510987\n",
      "第7轮训练精度：0.9402\n",
      "------\n",
      "第7轮测试用时：2.2925543785095215 s\n",
      "第7轮测试loss：1.1295666851043702\n",
      "第7轮测试精度：0.7142\n",
      "------\n",
      "第8轮训练用时：14.152458667755127 s\n",
      "第8轮训练loss：0.1358924177980423\n",
      "第8轮训练精度：0.95432\n",
      "------\n",
      "第8轮测试用时：2.287228584289551 s\n",
      "第8轮测试loss：1.1226089702606201\n",
      "第8轮测试精度：0.7353\n",
      "------\n",
      "第9轮训练用时：14.181235074996948 s\n",
      "第9轮训练loss：0.1065045474100113\n",
      "第9轮训练精度：0.96406\n",
      "------\n",
      "第9轮测试用时：2.2778074741363525 s\n",
      "第9轮测试loss：0.9503556213378906\n",
      "第9轮测试精度：0.7687\n",
      "------\n",
      "第10轮训练用时：14.153496742248535 s\n",
      "第10轮训练loss：0.08724390973329545\n",
      "第10轮训练精度：0.97052\n",
      "------\n",
      "第10轮测试用时：2.2487542629241943 s\n",
      "第10轮测试loss：1.0234093978881835\n",
      "第10轮测试精度：0.7669\n",
      "------\n",
      "第11轮训练用时：14.188186645507812 s\n",
      "第11轮训练loss：0.07506496063232422\n",
      "第11轮训练精度：0.97432\n",
      "------\n",
      "第11轮测试用时：2.2685508728027344 s\n",
      "第11轮测试loss：1.0188864192962646\n",
      "第11轮测试精度：0.7701\n",
      "------\n",
      "第12轮训练用时：14.163139820098877 s\n",
      "第12轮训练loss：0.052648978115320204\n",
      "第12轮训练精度：0.98246\n",
      "------\n",
      "第12轮测试用时：2.272318124771118 s\n",
      "第12轮测试loss：1.0598801008224488\n",
      "第12轮测试精度：0.768\n",
      "------\n",
      "第13轮训练用时：14.178502082824707 s\n",
      "第13轮训练loss：0.04728093039393425\n",
      "第13轮训练精度：0.98408\n",
      "------\n",
      "第13轮测试用时：2.278552532196045 s\n",
      "第13轮测试loss：1.1991549434661866\n",
      "第13轮测试精度：0.7547\n",
      "------\n",
      "第14轮训练用时：14.174543142318726 s\n",
      "第14轮训练loss：0.036711175911426544\n",
      "第14轮训练精度：0.9873\n",
      "------\n",
      "第14轮测试用时：2.3062613010406494 s\n",
      "第14轮测试loss：1.2436473808288575\n",
      "第14轮测试精度：0.7587\n",
      "------\n",
      "第15轮训练用时：14.185297727584839 s\n",
      "第15轮训练loss：0.03486346439361572\n",
      "第15轮训练精度：0.9884\n",
      "------\n",
      "第15轮测试用时：2.285529613494873 s\n",
      "第15轮测试loss：1.134100531578064\n",
      "第15轮测试精度：0.7783\n",
      "------\n",
      "第16轮训练用时：14.170045614242554 s\n",
      "第16轮训练loss：0.039531714814305306\n",
      "第16轮训练精度：0.98676\n",
      "------\n",
      "第16轮测试用时：2.3928141593933105 s\n",
      "第16轮测试loss：1.1340660545349122\n",
      "第16轮测试精度：0.7805\n",
      "------\n",
      "第17轮训练用时：14.158070802688599 s\n",
      "第17轮训练loss：0.03143463790953159\n",
      "第17轮训练精度：0.98956\n",
      "------\n",
      "第17轮测试用时：2.2641143798828125 s\n",
      "第17轮测试loss：1.2109551231384277\n",
      "第17轮测试精度：0.768\n",
      "------\n",
      "第18轮训练用时：14.206767082214355 s\n",
      "第18轮训练loss：0.025556572340130805\n",
      "第18轮训练精度：0.99184\n",
      "------\n",
      "第18轮测试用时：2.464784622192383 s\n",
      "第18轮测试loss：1.0644898120880126\n",
      "第18轮测试精度：0.7985\n",
      "------\n",
      "第19轮训练用时：14.15512204170227 s\n",
      "第19轮训练loss：0.018151097567379475\n",
      "第19轮训练精度：0.99392\n",
      "------\n",
      "第19轮测试用时：2.3127596378326416 s\n",
      "第19轮测试loss：1.2681780355453491\n",
      "第19轮测试精度：0.7744\n",
      "------\n",
      "第20轮训练用时：14.220973014831543 s\n",
      "第20轮训练loss：0.013937484435737134\n",
      "第20轮训练精度：0.99554\n",
      "------\n",
      "第20轮测试用时：2.2767977714538574 s\n",
      "第20轮测试loss：1.1242127571105958\n",
      "第20轮测试精度：0.7972\n",
      "------\n",
      "第21轮训练用时：14.18807578086853 s\n",
      "第21轮训练loss：0.01835785879775882\n",
      "第21轮训练精度：0.99396\n",
      "------\n",
      "第21轮测试用时：2.281655788421631 s\n",
      "第21轮测试loss：1.279755305480957\n",
      "第21轮测试精度：0.7768\n",
      "------\n",
      "第22轮训练用时：14.202364206314087 s\n",
      "第22轮训练loss：0.02459119977772236\n",
      "第22轮训练精度：0.99192\n",
      "------\n",
      "第22轮测试用时：2.3524081707000732 s\n",
      "第22轮测试loss：1.185959701347351\n",
      "第22轮测试精度：0.7874\n",
      "------\n",
      "第23轮训练用时：14.244802951812744 s\n",
      "第23轮训练loss：0.02674837143778801\n",
      "第23轮训练精度：0.99112\n",
      "------\n",
      "第23轮测试用时：2.2736380100250244 s\n",
      "第23轮测试loss：1.0887953372955321\n",
      "第23轮测试精度：0.7997\n",
      "------\n",
      "第24轮训练用时：14.222193956375122 s\n",
      "第24轮训练loss：0.019867311609089375\n",
      "第24轮训练精度：0.9936\n",
      "------\n",
      "第24轮测试用时：2.267754554748535 s\n",
      "第24轮测试loss：1.0746454006195068\n",
      "第24轮测试精度：0.8023\n",
      "------\n",
      "第25轮训练用时：14.16916275024414 s\n",
      "第25轮训练loss：0.016493817646354436\n",
      "第25轮训练精度：0.99428\n",
      "------\n",
      "第25轮测试用时：2.3157761096954346 s\n",
      "第25轮测试loss：1.1314417352676391\n",
      "第25轮测试精度：0.7995\n",
      "------\n",
      "第26轮训练用时：14.17615294456482 s\n",
      "第26轮训练loss：0.009545815300419926\n",
      "第26轮训练精度：0.9972\n",
      "------\n",
      "第26轮测试用时：2.266488552093506 s\n",
      "第26轮测试loss：1.109633180999756\n",
      "第26轮测试精度：0.8014\n",
      "------\n",
      "第27轮训练用时：14.186076879501343 s\n",
      "第27轮训练loss：0.005301013141311705\n",
      "第27轮训练精度：0.99858\n",
      "------\n",
      "第27轮测试用时：2.2673637866973877 s\n",
      "第27轮测试loss：1.1268505191802978\n",
      "第27轮测试精度：0.8034\n",
      "------\n",
      "第28轮训练用时：14.199875116348267 s\n",
      "第28轮训练loss：0.005551986647583544\n",
      "第28轮训练精度：0.99838\n",
      "------\n",
      "第28轮测试用时：2.268481492996216 s\n",
      "第28轮测试loss：1.1217255283355714\n",
      "第28轮测试精度：0.8126\n",
      "------\n",
      "第29轮训练用时：14.165087699890137 s\n",
      "第29轮训练loss：0.005763132807444781\n",
      "第29轮训练精度：0.99808\n",
      "------\n",
      "第29轮测试用时：2.286205768585205 s\n",
      "第29轮测试loss：1.1909760669708251\n",
      "第29轮测试精度：0.8006\n",
      "------\n",
      "第30轮训练用时：14.218900203704834 s\n",
      "第30轮训练loss：0.006411183550618589\n",
      "第30轮训练精度：0.99792\n",
      "------\n",
      "第30轮测试用时：2.2721900939941406 s\n",
      "第30轮测试loss：1.1887778072357178\n",
      "第30轮测试精度：0.8041\n",
      "------\n",
      "第31轮训练用时：14.155977487564087 s\n",
      "第31轮训练loss：0.0036888319079205396\n",
      "第31轮训练精度：0.99898\n",
      "------\n",
      "第31轮测试用时：2.254350423812866 s\n",
      "第31轮测试loss：1.1523178760528565\n",
      "第31轮测试精度：0.8093\n",
      "------\n",
      "第32轮训练用时：14.169361352920532 s\n",
      "第32轮训练loss：0.006332453640308231\n",
      "第32轮训练精度：0.99812\n",
      "------\n",
      "第32轮测试用时：2.2559592723846436 s\n",
      "第32轮测试loss：1.1737571712493897\n",
      "第32轮测试精度：0.8094\n",
      "------\n",
      "第33轮训练用时：14.165345191955566 s\n",
      "第33轮训练loss：0.010105430382359773\n",
      "第33轮训练精度：0.99688\n",
      "------\n",
      "第33轮测试用时：2.3567817211151123 s\n",
      "第33轮测试loss：1.1981683708190918\n",
      "第33轮测试精度：0.803\n",
      "------\n",
      "第34轮训练用时：14.169023752212524 s\n",
      "第34轮训练loss：0.012827502208128571\n",
      "第34轮训练精度：0.9957\n",
      "------\n",
      "第34轮测试用时：2.2709240913391113 s\n",
      "第34轮测试loss：1.2470752641677856\n",
      "第34轮测试精度：0.7963\n",
      "------\n",
      "第35轮训练用时：14.155800104141235 s\n",
      "第35轮训练loss：0.008988198314867914\n",
      "第35轮训练精度：0.9972\n",
      "------\n",
      "第35轮测试用时：2.3145947456359863 s\n",
      "第35轮测试loss：1.2301520362854004\n",
      "第35轮测试精度：0.7968\n",
      "------\n",
      "第36轮训练用时：14.153849601745605 s\n",
      "第36轮训练loss：0.006832718424689956\n",
      "第36轮训练精度：0.99782\n",
      "------\n",
      "第36轮测试用时：2.2908637523651123 s\n",
      "第36轮测试loss：1.1969097913742066\n",
      "第36轮测试精度：0.8023\n",
      "------\n",
      "第37轮训练用时：14.191701650619507 s\n",
      "第37轮训练loss：0.015182318208664655\n",
      "第37轮训练精度：0.99512\n",
      "------\n",
      "第37轮测试用时：2.2712509632110596 s\n",
      "第37轮测试loss：1.2395636724472046\n",
      "第37轮测试精度：0.7946\n",
      "------\n",
      "第38轮训练用时：14.181225538253784 s\n",
      "第38轮训练loss：0.011029123407676816\n",
      "第38轮训练精度：0.99662\n",
      "------\n",
      "第38轮测试用时：2.301096200942993 s\n",
      "第38轮测试loss：1.2112833618164063\n",
      "第38轮测试精度：0.793\n",
      "------\n",
      "第39轮训练用时：14.207796335220337 s\n",
      "第39轮训练loss：0.00532042785808444\n",
      "第39轮训练精度：0.99828\n",
      "------\n",
      "第39轮测试用时：2.3024678230285645 s\n",
      "第39轮测试loss：1.1780060503005982\n",
      "第39轮测试精度：0.8044\n",
      "------\n",
      "第40轮训练用时：14.185658693313599 s\n",
      "第40轮训练loss：0.010408173738755286\n",
      "第40轮训练精度：0.99656\n",
      "------\n",
      "第40轮测试用时：2.3073174953460693 s\n",
      "第40轮测试loss：1.4035098236083985\n",
      "第40轮测试精度：0.783\n",
      "------\n",
      "第41轮训练用时：14.250556230545044 s\n",
      "第41轮训练loss：0.015292802033051849\n",
      "第41轮训练精度：0.99504\n",
      "------\n",
      "第41轮测试用时：2.2686450481414795 s\n",
      "第41轮测试loss：1.2817494789123536\n",
      "第41轮测试精度：0.7964\n",
      "------\n",
      "第42轮训练用时：14.160066366195679 s\n",
      "第42轮训练loss：0.009899518830887973\n",
      "第42轮训练精度：0.99686\n",
      "------\n",
      "第42轮测试用时：2.279996395111084 s\n",
      "第42轮测试loss：1.4274443740844727\n",
      "第42轮测试精度：0.7744\n",
      "------\n",
      "第43轮训练用时：14.218758821487427 s\n",
      "第43轮训练loss：0.010372496769391\n",
      "第43轮训练精度：0.99652\n",
      "------\n",
      "第43轮测试用时：2.3030507564544678 s\n",
      "第43轮测试loss：1.260033082962036\n",
      "第43轮测试精度：0.7978\n",
      "------\n",
      "第44轮训练用时：14.197827577590942 s\n",
      "第44轮训练loss：0.006083117229240015\n",
      "第44轮训练精度：0.99796\n",
      "------\n",
      "第44轮测试用时：2.274169445037842 s\n",
      "第44轮测试loss：1.1482084156036376\n",
      "第44轮测试精度：0.8116\n",
      "------\n",
      "第45轮训练用时：14.23676609992981 s\n",
      "第45轮训练loss：0.0024223943696916102\n",
      "第45轮训练精度：0.99938\n",
      "------\n",
      "第45轮测试用时：2.2737271785736084 s\n",
      "第45轮测试loss：1.1415780532836914\n",
      "第45轮测试精度：0.8143\n",
      "------\n",
      "第46轮训练用时：14.184333801269531 s\n",
      "第46轮训练loss：0.0031866007357183846\n",
      "第46轮训练精度：0.9991\n",
      "------\n",
      "第46轮测试用时：2.2653987407684326 s\n",
      "第46轮测试loss：1.2528513103485108\n",
      "第46轮测试精度：0.7996\n",
      "------\n",
      "第47轮训练用时：14.181193590164185 s\n",
      "第47轮训练loss：0.0015480179109447635\n",
      "第47轮训练精度：0.99956\n",
      "------\n",
      "第47轮测试用时：2.263620615005493 s\n",
      "第47轮测试loss：1.143106476211548\n",
      "第47轮测试精度：0.8129\n",
      "------\n",
      "第48轮训练用时：14.159244775772095 s\n",
      "第48轮训练loss：0.0032453502497123555\n",
      "第48轮训练精度：0.99902\n",
      "------\n",
      "第48轮测试用时：2.274949312210083 s\n",
      "第48轮测试loss：1.2091034755706787\n",
      "第48轮测试精度：0.8087\n",
      "------\n",
      "第49轮训练用时：14.204683065414429 s\n",
      "第49轮训练loss：0.0035887903581932187\n",
      "第49轮训练精度：0.99904\n",
      "------\n",
      "第49轮测试用时：2.301504135131836 s\n",
      "第49轮测试loss：1.2460267383575439\n",
      "第49轮测试精度：0.8056\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "# 保存训练数据绘图\n",
    "train_loss_list = []\n",
    "train_accuracy_list = []\n",
    "\n",
    "test_loss_list = []\n",
    "test_accuracy_list = []\n",
    "\n",
    "epoch_step = []\n",
    "print(\"start train.\")\n",
    "for i in range(epoch):\n",
    "    epoch_step.append(i)\n",
    "    \n",
    "    mymod.train()\n",
    "    start_time = time.time()\n",
    "    total_train_loss_pre_epoch = 0\n",
    "    total_train_accuracy_pre_epoch = 0\n",
    "    for data in train_dataloader:\n",
    "        imgs, targets = data\n",
    "        # print(imgs.shape)\n",
    "        imgs = imgs.to(device)\n",
    "        targets = targets.to(device)\n",
    "        outputs = mymod(imgs)\n",
    "        accuracy = (outputs.argmax(1) == targets).sum()\n",
    "        total_train_accuracy_pre_epoch += accuracy.item()\n",
    "\n",
    "        loss = loss_fn(outputs, targets)\n",
    "        total_train_loss_pre_epoch += loss.item() * targets.size(0)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    end_train_time = time.time()\n",
    "    print(\"第{}轮训练用时：{} s\".format(i, end_train_time - start_time))\n",
    "    print(\"第{}轮训练loss：{}\".format(i, total_train_loss_pre_epoch / train_data_size))\n",
    "    print(\"第{}轮训练精度：{}\".format(i, total_train_accuracy_pre_epoch / train_data_size))\n",
    "    print(\"------\")\n",
    "    train_loss_list.append(total_train_loss_pre_epoch / train_data_size)\n",
    "    train_accuracy_list.append(total_train_accuracy_pre_epoch / train_data_size)\n",
    "    \n",
    "    writer.add_scalar(\"train_loss\", total_train_loss_pre_epoch / train_data_size, i)\n",
    "    writer.add_scalar(\"train_acc\", total_train_accuracy_pre_epoch / train_data_size, i)\n",
    "    \n",
    "    \n",
    "    mymod.eval()\n",
    "    total_test_loss_pre_epoch = 0\n",
    "    total_test_accuracy_pre_epoch = 0\n",
    "    # 去掉梯度\n",
    "    with torch.no_grad():\n",
    "        for data in test_dataloader:\n",
    "            imgs, targets = data\n",
    "            imgs = imgs.to(device)\n",
    "            targets = targets.to(device)\n",
    "            outputs = mymod(imgs)\n",
    "\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            total_test_loss_pre_epoch += loss.item() * targets.size(0)\n",
    "            # 统计在测试集上正确的个数，然后累加起来\n",
    "            accuracy = (outputs.argmax(1) == targets).sum()\n",
    "            total_test_accuracy_pre_epoch += accuracy.item()\n",
    "\n",
    "\n",
    "    end_test_time = time.time()\n",
    "    print(\"第{}轮测试用时：{} s\".format(i, end_test_time - end_train_time))\n",
    "    print(\"第{}轮测试loss：{}\".format(i, total_test_loss_pre_epoch / test_data_size))\n",
    "    print(\"第{}轮测试精度：{}\".format(i, total_test_accuracy_pre_epoch / test_data_size))\n",
    "    print(\"------\")\n",
    "    test_loss_list.append(total_test_loss_pre_epoch / test_data_size)\n",
    "    test_accuracy_list.append(total_test_accuracy_pre_epoch / test_data_size)\n",
    "    \n",
    "    writer.add_scalar(\"test_loss\", total_test_loss_pre_epoch / test_data_size, i)\n",
    "    writer.add_scalar(\"test_acc\", total_test_accuracy_pre_epoch / test_data_size, i)\n",
    "\n",
    "\n",
    "torch.save(mymod, \"./{}.pth\".format(save_model_name))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9470575",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T10:07:07.762012Z",
     "iopub.status.busy": "2024-05-12T10:07:07.761710Z",
     "iopub.status.idle": "2024-05-12T10:07:09.394150Z",
     "shell.execute_reply": "2024-05-12T10:07:09.393198Z"
    },
    "papermill": {
     "duration": 1.649072,
     "end_time": "2024-05-12T10:07:09.396239",
     "exception": false,
     "start_time": "2024-05-12T10:07:07.747167",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB+OUlEQVR4nO3deXhU5dk/8O+ZPdtkX0nYAggoIIsgbkXZBEvdWqu21dpaqxU3+rOvtG60Wlpbra2lr7bWve4LfduikqIIyCabskMgkBCyb5Nt9vP748w5mZBJMjM5s38/1+XVZnJyzsmTYeae+7mf+xFEURRBREREFCc0kb4BIiIiIjUxuCEiIqK4wuCGiIiI4gqDGyIiIoorDG6IiIgorjC4ISIiorjC4IaIiIjiCoMbIiIiiisMboiIiCiuMLghooQgCAIeffTRSN8GEYUBgxsiUsXmzZvx6KOPorW1NWTX+PWvf43Vq1eH7PxEFB8Y3BCRKjZv3owVK1YwuCGiiGNwQ0RERHGFwQ0RDdmjjz6K+++/HwAwatQoCIIAQRBw4sQJAMBrr72G6dOnIykpCVlZWbj++utRVVXV6xxHjx7Ftddei4KCAphMJhQXF+P6669HW1sbAKlmprOzEy+//LJy/u9///tDuu/du3dj0aJFMJvNSE1Nxdy5c7F169ZexzgcDqxYsQJjx46FyWRCdnY2LrroIpSVlSnH1NbW4pZbbkFxcTGMRiMKCwtx5ZVXKr8/EYWXLtI3QESx75prrsGRI0fwxhtv4A9/+ANycnIAALm5uXj88cfx0EMP4brrrsOtt96KhoYGPPPMM7jkkkuwe/duZGRkwG63Y+HChbDZbLjrrrtQUFCA6upq/Pvf/0ZrayvS09Px6quv4tZbb8XMmTNx2223AQBKS0uDvuf9+/fj4osvhtlsxs9+9jPo9Xo899xzmDNnDj777DPMmjULgBS4rVy5Urm2xWLBjh07sGvXLsyfPx8AcO2112L//v246667MHLkSNTX16OsrAyVlZUYOXLk0AaXiAInEhGp4He/+50IQKyoqFAeO3HihKjVasXHH3+817F79+4VdTqd8vju3btFAOI777wz4DVSUlLEm2++Oaj7AyA+8sgjytdXXXWVaDAYxGPHjimPnT59WkxLSxMvueQS5bEpU6aIV1xxRb/nbWlpEQGIv/vd74K6LyJSH6eliChk3n//fbjdblx33XVobGxU/isoKMDYsWPx6aefAgDS09MBAB9//DG6urpCfl8ulwtr167FVVddhdGjRyuPFxYW4sYbb8SmTZtgsVgAABkZGdi/fz+OHj3q81xJSUkwGAxYv349WlpaQn7vRDQ4BjdEFDJHjx6FKIoYO3YscnNze/138OBB1NfXA5DqdJYtW4bnn38eOTk5WLhwIVatWqXU26itoaEBXV1dOOuss/p8b8KECXC73UpN0C9/+Uu0trZi3LhxmDRpEu6//3589dVXyvFGoxG//e1v8eGHHyI/Px+XXHIJnnjiCdTW1obk3olocAxuiChk3G43BEHARx99hLKysj7/Pffcc8qxTz75JL766iv8/Oc/R3d3N+6++26cffbZOHXqVAR/A+CSSy7BsWPH8MILL+Ccc87B888/j2nTpuH5559Xjrn33ntx5MgRrFy5EiaTCQ899BAmTJiA3bt3R/DOiRIXgxsiUoUgCH0eKy0thSiKGDVqFObNm9fnv/PPP7/X8ZMmTcKDDz6IDRs2YOPGjaiursazzz474DWCkZubi+TkZBw+fLjP9w4dOgSNRoOSkhLlsaysLNxyyy144403UFVVhcmTJ/fpdlxaWoqf/vSnWLt2Lfbt2we73Y4nn3xSlfslosAwuCEiVaSkpABAryZ+11xzDbRaLVasWAFRFHsdL4oimpqaAAAWiwVOp7PX9ydNmgSNRgObzdbrGmo0CdRqtViwYAH++c9/9lquXVdXh9dffx0XXXQRzGYzACj3KEtNTcWYMWOU++rq6oLVau11TGlpKdLS0nrdOxGFD5eCE5Eqpk+fDgD4xS9+geuvvx56vR5LlizBY489huXLl+PEiRO46qqrkJaWhoqKCnzwwQe47bbb8P/+3//DJ598gqVLl+Jb3/oWxo0bB6fTiVdffRVarRbXXnttr2v897//xVNPPYWioiKMGjVKWbIdqMceewxlZWW46KKL8JOf/AQ6nQ7PPfccbDYbnnjiCeW4iRMnYs6cOZg+fTqysrKwY8cOvPvuu1i6dCkA4MiRI5g7dy6uu+46TJw4ETqdDh988AHq6upw/fXXD2FEiShokV2sRUTx5Fe/+pU4bNgwUaPR9FoW/t5774kXXXSRmJKSIqakpIjjx48X77zzTvHw4cOiKIri8ePHxR/84AdiaWmpaDKZxKysLPHSSy8V//vf//Y6/6FDh8RLLrlETEpKEgEEtCwcZywFF0VR3LVrl7hw4UIxNTVVTE5OFi+99FJx8+bNvY557LHHxJkzZ4oZGRliUlKSOH78ePHxxx8X7Xa7KIqi2NjYKN55553i+PHjxZSUFDE9PV2cNWuW+Pbbbwc2eESkGkEUz8gVExEREcUw1twQERFRXGHNDRHFLJfLhYaGhgGPSU1NRWpqapjuiIiiAYMbIopZVVVVGDVq1IDHPPLII32WbRNRfGNwQ0Qxq6CgoNfu3L54b69ARImBBcVEREQUV1hQTERERHEl4aal3G43Tp8+jbS0NNVauRMREVFoiaKI9vZ2FBUVQaMZODeTcMHN6dOne+0ZQ0RERLGjqqoKxcXFAx6TcMFNWloaAGlw5L1j1OJwOLB27VosWLAAer1e1XNTXxzv8OJ4hxfHO7w43uEVzHhbLBaUlJQo7+MDSbjgRp6KMpvNIQlukpOTYTab+Y8jDDje4cXxDi+Od3hxvMNrKOPtT0kJC4qJiIgorjC4ISIiorjC4IaIiIjiCoMbIiIiiisMboiIiCiuMLghIiKiuMLghoiIiOIKgxsiIiKKKwxuiIiIKK4wuCEiIqK4wuCGiIiI4gqDGyIiIoorDG6IiIhU1G13RfoWEh6DGyIiIpW8uuUEzn7kI/z3QF2kbyWhMbghIiJSycajjXCLwNbjTZG+lYTG4IaIiEglp1q6AQC1FmuE7ySxMbghIiJSSXWrJ7hpY3ATSQxuiIiIVGCxOtDW7QDAzE2kMbghIiJSQbVnSgoA6ixWuN1iBO8msTG4ISIiUoF3cONwiWjqtEfwbhIbgxsiIiIVnGrp6vV1HaemIobBDRERkQpOeWVuAKCGRcURw+CGiIhIBfJKKRmLiiOHwQ0REZEK5MxNUboJAFDb1j3Q4RRCEQ1uNmzYgCVLlqCoqAiCIGD16tV+/+znn38OnU6Hc889N2T3R0RE5C+55mb6yCwAQG2bLZK3k9AiGtx0dnZiypQpWLVqVUA/19raiptuuglz584N0Z0REVEi+fnq/XjxsCbo5dudNidauqQeNzNGZAIAai3M3ESKLpIXX7RoERYtWhTwz91+++248cYbodVqA8r2EBERnanb7sI7O6sBaFDV0o0xBYaAzyHX25hNOozNTwXALsWRFNHgJhgvvvgijh8/jtdeew2PPfbYoMfbbDbYbD2pQYvFAgBwOBxwOByq3pt8PrXPS75xvMOL4x1eHO/wqfeqjTlaZ8GI7OSAz3GioR0AUJSRhNwU6a21ps0Ku90OQRDUudE4EszzO5BjYyq4OXr0KB544AFs3LgROp1/t75y5UqsWLGiz+Nr165FcnLgT2B/lJWVheS85BvHO7w43uHF8Q69qg5Afjtcu2U37CcCn5raWCsA0EJva8OezZ8B0KHL7sL7//oQSTH1ThtegTy/u7q6Bj/II2aG3OVy4cYbb8SKFSswbtw4v39u+fLlWLZsmfK1xWJBSUkJFixYALPZrOo9OhwOlJWVYf78+dDr9aqem/rieIcXxzu8ON7hs/FoI7B3FwDAmFOCxYvPCfgcez8+AlScwLTxI3HV4vF4bO8naOt2YvL5l2BsXqratxzzgnl+yzMv/oiZ4Ka9vR07duzA7t27sXTpUgCA2+2GKIrQ6XRYu3YtLrvssj4/ZzQaYTQa+zyu1+tD9oIRynNTXxzv8OJ4hxfHO/TabC7l/1c2W4Ma7xrPyqiSrBTo9XoUmJPQ1t2Oxk4nJvLv169Ant+B/F1iJrgxm83Yu3dvr8f+8pe/4JNPPsG7776LUaNGRejOiIgoljV39tRyVDT5P/Xh7ZSnoLg4Uyp3KEg34XBdOxv5RUhEg5uOjg6Ul5crX1dUVGDPnj3IysrC8OHDsXz5clRXV+OVV16BRqPBOef0ThXm5eXBZDL1eZyIiMhfzZ09i05q2qywOlww6bUBnaPa0+OmODMJAFBglhv5MbiJhIj2udmxYwemTp2KqVOnAgCWLVuGqVOn4uGHHwYA1NTUoLKyMpK3SEREcc47cwMAJ5o6A/r5brsLjR3SDuBKcCN3KWbmJiIimrmZM2cORLH/qvSXXnppwJ9/9NFH8eijj6p7U0RElFBaOu29vq5o6MT4Av8XnMg9blKNOqQnSXUhhenM3EQS95YiIqKE1uwJbgwa6cP28cbAMjenvKak5J42+Z7ghjuDRwaDGyIiSmjNXVJwMyJVCm4qAg5u5GLiJOUxOXNTx2mpiGBwQ0RECU3O3IxKk74ONLiRp6WGZfQEN3JBcXOnHVaHy+fPUegwuCEiooTlcoto9WRuRqcNNXPT0/U+PUkPk156i623cHfwcGNwQ0RECaut2wF5I/ARnuCmudOuBDz+OHXGMnAAEARByd7UtHF38HBjcENERAlLnpIym3RI1gH5ZqmjfSDZm2pP5maYV3ADcDl4JDG4ISKihCUHN5nJBgDAKM+O4P4GN1aHC/Xt0rST97QUwEZ+kcTghoiIEpYc3GSlSP1pRuakAPA/uDntKSZONmiRmdx776OCdCmTw+Xg4cfghoiIEtZQMzfeK6XkHjcyLgePHAY3RESUsFq65MyNFNwEmrnx1eNGlm9mI79IYXBDREQJq6lDztx4pqWyejI3A20PJOtZKZXc53vM3EQOgxsiIkpYZ2ZuijOToNUI6LL3FAoPpL+VUkDPaqn6dhtc7sEDJVIPgxsiIkpYTZ29MzcGnQYlnkDleMPgU1MDTUvlpBqh1QhwuUU0drCRXzgxuCEiooQl7wie6cncAMCoAOpufHUnlmk1AvLSpL45rLsJLwY3RESUsJSl4F7LuEflpAIAKho7BvxZu9ONunYpaPHeV8qb0siPwU1YMbghIqKE1ewrc5PrX+ampq0boggYdRrkpBp8HtPTyI9bMIQTgxsiIkpI3XYXuj07dmcl9wQnoz3TUscHCW68623O7HEjkzM3NVwxFVYMboiIKCE1e1ZK6bUCUo1a5XG55qayqQtOl7vfn+9ZKdW33kamLAfntFRYMbghIqKE1NLZswzcO/NSYDbBpNfA6RaV7IwvvnYDPxMb+UUGgxsiIkpITWdsvSDTaASMzB687magZeCyQs/+UmzkF14MboiIKCHJmZtsH8XA/iwHP+W1r1R/CrwyN/50PCZ1MLghIqKE1F/mBvAvuKkeoMeNLM8s9bmxOd1o63YEfa8UGAY3RESUkJTMTUrgwY3D5UaNZ3l3yQDTUia9VtnagXU34cPghoiIElKTjx43stGD9LqpbbPCLQIGrQY5qcYBr9PT64bBTbgwuCEiooQ0cOZG6lJc3doNq6cXjrdTXhtmajS+e9zI5OXgtSwqDhsGN0RElJB8dSeWZSbrkZ4kbclwoqlv9safZeCy/HQuBw83BjdERJSQ5CZ+WT6CG0EQeupufOwOXu3HSilZoZmN/MKNwQ0RESWk5s7+gxtg4G0Y/OlxI8vnFgxhx+CGiIgSjsstolXO3PhYCg4MvGKqZ1qq/2XgMm7BEH4MboiIKOFYuh1we3rq+aq5AQbeHVyelvInc9PTyI87g4cLgxsiIko48jLwNJMOeq3vt8L+MjdOlxs1rVIWZpg/wY0nc2OxOtFldwZ9z+Q/BjdERJRwWrr6XwYuk/eXau60o62rp7twXbsNTrcIvVZAXppp0GulmfRIMUi7jrPXTXgwuCEiooTT1NH/MnBZilGHfM/2CRVey8HlbReKMpKgHaTHjUzO3jC4CQ8GN0RElHD8ydwA3lNTHcpjcjGxP8vAZfLu4GzkFx4MboiIKOE0D7Bppje5U7F3r5tAloHL8s1s5BdODG6IiCjhKD1uUgcObnz1uvFnN/AzKcvBmbkJCwY3RESUcJTgZtDMTd8VU6daA5+W4hYM4cXghoiIEs5g3Yll3r1uRFFqjBPMtFQhdwYPKwY3RESUcPwNbkoyk6HVCOiyu1DfboPbLeK03MAvy/9pqQLuDB5WDG6IiCjh+BvcGHQalHgyNMcbOlHfboPDJUKrEZCfZvT7enJw09hhg8PlDvKuyV8RDW42bNiAJUuWoKioCIIgYPXq1QMev2nTJlx44YXIzs5GUlISxo8fjz/84Q/huVkiIoob/gY3QO+6m2pPvU1hugm6fjob+5KVbIBeK0AUgfp2WxB3TIGIaHDT2dmJKVOmYNWqVX4dn5KSgqVLl2LDhg04ePAgHnzwQTz44IP461//GuI7JSKieNFtd6Hb4QIwcBM/mbIcvLEjqHobANBoBGU5eC33mAo5XSQvvmjRIixatMjv46dOnYqpU6cqX48cORLvv/8+Nm7ciNtuuy0Ut0hERHFGbuCn1wpIMw7+NjgqR6qtqWjsRIZnddWwDP/rbWSF6SacaulGbRszN6EW0eBmqHbv3o3Nmzfjscce6/cYm80Gm63niWSxWAAADocDDoejvx8Linw+tc9LvnG8w4vjHV4c79Cpb5OmljKTDXA6pY0sBxrvkkwp43K8oVPpaFyUbgj4b5OXKtXoVLd0JvzfNZjndyDHxmRwU1xcjIaGBjidTjz66KO49dZb+z125cqVWLFiRZ/H165di+TkwCNvf5SVlYXkvOQbxzu8ON7hxfFW36FWAYAWOpcVa9as6fU9X+PdbAMAHU42dUBrbwegQVPlUaxZcySg63Y1aQBosGXPQeS37g/29uNKIM/vrq4uv4+NyeBm48aN6OjowNatW/HAAw9gzJgxuOGGG3weu3z5cixbtkz52mKxoKSkBAsWLIDZbFb1vhwOB8rKyjB//nzo9XpVz019cbzDi+MdXhzv0HF8WQMc3IsRBdlYvHiG9NgA4+12i/jNV+tgc7pR1a0F4MaiS2bh/NFZAV23bvNJfFpzGMnZRVi8eLJav05MCub5Lc+8+CMmg5tRo0YBACZNmoS6ujo8+uij/QY3RqMRRmPf5Xp6vT5kLxihPDf1xfEOL453eHG81WexSsXE2anGPmPb33iPyknBodp2WB3SMu6RuWkB/12GZUqrrurabfybegTy/A5kzGK+z43b7e5VU0NERDSQQJaBy+Tl4ACgEXr61gSiIF36oM1GfqEX0cxNR0cHysvLla8rKiqwZ88eZGVlYfjw4Vi+fDmqq6vxyiuvAABWrVqF4cOHY/z48QCkPjm///3vcffdd0fk/omIKPY0dw0tuCkwm6APoMeN8nPp0vLxOosVbrcIjUYI+Bzkn4gGNzt27MCll16qfC3Xxtx888146aWXUFNTg8rKSuX7brcby5cvR0VFBXQ6HUpLS/Hb3/4WP/7xj8N+70REFJuaO4YW3ASyG7i3vDQjBAFwuEQ0d9mRk+p/h2MKTESDmzlz5igbkfny0ksv9fr6rrvuwl133RXiuwqO3enG4dp2HPO/3omIiCIgmMzN6Fzv4CawBn4yvVaDnFQjGtptqG2zMrgJoZivuYkWdRYrlqzagv89oB0wYCMioshSam6SA8ncpCr/f1iQwQ0gNfIDuDt4qDG4UYn8CcAhSrvHEhFRdGqRg5tU/4ObzGQ9zCZpsiPYzA0AZQuGGhYVhxSDG5UkG7Qw6qThlFOeRInmX1+exs8/2Mtdjylqud2isv1CIJkbQRAwpSQDADCxMD3o6/dkbri/VCjFZJ+baCQIArJSDKhps6K504HRkb4hogj47UeHcKqlG/Mn5OPS8XmRvh2iPtq6HXB7Kgf82TTT25+un4oTTZ2YVBx8cNOzeSZbmIQSMzcqykyWGgzJ87lEicTlFlHjqSPYV90W4bsh8q3J8/qcZtIFvJw7M8WAqcMzh3R9JXNjYeYmlBjcqEiuu2FwQ4movt0Kl+cj8f7TXDZI0akliJVSaiows6A4HBjcqEiev23pSuzdXikxnW7t+SS6v4aZG4pOwXQnVlMBV0uFBYMbFWWlcFqKEtfp1p4X66rmbrQxyKcoFMwycDXJwU2n3YV2K/+NhAqDGxUp01JcLUUxxOpw4Uev7MAb2ysHP3gA3pkbgNkbik6RztwkG3TKknJmb0KHwY2KWHNDsWhbRTPKDtThz5+UD37wAM4Mbg6w7oaiUKSDGwAo9OwxVcPgJmQY3KioZ7UUU40UOxrapSWptRYrnEPoT3Pa80Kdb5ZayrOomKJRSxQEN/nKiikGN6HC4EZFzNxQLGrskIIb76XcwZAzN/Mm5AMA9p/mtBRFH3kpeKA9btRUyBVTIcfgRkVcLUWxqKmjp5nYqZbge2/Iwc38iVJwU17fgW5uRUJRRl4Kns3MTVxjcKMiOXPTYXPC5uSLOsWGxo6eTOOplq6gztFtdylB/dThmchOMcAtAodqOTVF0aWpI/KZmyJPcDOUDxM0MAY3KjKbdNBAamLWwrobihGNKmRuTnv2yUk1SitBzh4mtadn3Q1Fm2jI3JTmSTuMH6vviNg9xDsGNyrSaAR4Wt2gqZP7hlBs6J25CTK48UxJFWWYIAgCzi4yA2BwQ9HF6nChyzNVGsnMzZhcKbipbu1Gp80ZsfuIZwxuVJbq2YqURcUUK3pnboKblpKDG3mJqxzcHGBRMUUR+XVZrxWQZozcvtGZKQYlc3S8oTPk13tq7WFc+JtPEqqAmcGNylL00rQUgxuKBW632Ou5GnzmRnrRLMqQgxtpWupgbTscQ1heTqQm+bmemWyAIAgRvRd5aupofXvIr/XuzlOobu3GhqMNIb9WtGBwo7JUz7QUgxuKBa3dDmWzSyD4Xjdy5mZYhlQoOSIrGalGHexON441sK4gXrjdIkRRHPzAKBUNDfxkYz3BTXmI6246bU6lB1WorxVNGNyojNNSFEvkKSmzSQeDThN0rxu5oFieltJoBEws9NTdVLPuJh7UW6yYtXIdvvf37bDE6J5Ikd4R3NuYMAU33h8ujtaFPkskiiJ++vaX+GhfDdzuyAXCDG5UlqoUFDO4SUTHGjrQ1h07L/yNnu7EeWYTij1TSsFMTdWcMS0FABNZVBxX3tl5Cg3tNmwqb8QNf93aqz9SrIiGZeAyJbgJcWbzaJ1XcBOGzM36ww14b9cpLHv7y4i+FjK4UVmqXHPTweAm0ew82YwFf9iAe97cHelb8VtjZ8+y2GGZcnATWFGxKIqoVqaleoKbnhVTLCqOdaIo4r1dpwAAOo2A/actuO65Lahpi60+LdGwDFw2Ni8NAHCyqQt2Z+jq0ryDp1Mt3eiyh251liiK+NMnRwEA3z1/RESDSAY3KuO0VOJ68fMTcLlFbDveHNF0bCDkzE1OmhHFmckAAs/cNHfaYfO8OOenG5XH5aLiA6ctMTMe5NuXp9pwvKETJr0G7//kAhSlm3CsoRPf/N8tONEYmtU+LreIqubgVu/1p8mroDjS8s1GpBp1cLlFnGgK3Yop78wNAByrD921Pi9vwu7KVhh1Gtx68aiQXccfDG5Uxj43iamxw4aP99cCALodLiWTEe3kmpvcVCOKM4OblpJrdHLTjDDqtMrjY/NTYdBq0G5zoirIJeYUHd7bKWVtLj+7AJOLM/DOHRdgVE4Kqlu78c1nt4SkE/Ufyo7g4ic+xQubKlQ7p7xpZnZq5IMbQRCUFVOhrLuRa26MOuntPpSrs+SszQ0zhyMvzRSy6/iDwY3KUnXhWQq+q7IFl/1+PdYdrAvpdcg/7+w4BYerJzsRK6sS5BqE7BSDV3ATWCBSrTTwS+r1uF6rwVkFUuqddTexy+Z04V9fnQYAXDOtGIA0/fj2j2djQqEZjR02fPu5rdhV2aLaNTttTry8+QQA4ImPD6GySZ3gOJoyN0BPM78zsytqsTpcOOnJCs05K1e6Vohem7Ydb8L2imYYtBrc/rXSkFwjEAxuVCYXFJ+5xFZtH++vxfHGTny4rzZk1yD/uN0i3theCQBIMUiZi3D0rlCDnLkZyrSU0p04ve8nNdbdxL5PDzWgtcuBfLMRF47JUR7PTTPizR+dj2nDM9DW7cB3n9+GzeWNqlzzg93VaPd07rU63Pj5B3tVWYLe0hk9NTeAlN0EQldUfKKpE25RWg15Qan0twtVIPXMJ+UAgG/NKEaBj9eCcGNwo7IUT82NKAKtXaHL3tRbpDel9hhdkhlPNpU3orK5C2kmHb47ewSA0L2AqE0uKM5JNaLEk7mpaesOqPGePC11ZuYGALdhiANyIfFVU4dBq+nd+C49WY9XfzgLF43JQZfdhe+/9AXW7h/aBy5RFPHKlhMAgO9fMBJGnQabyhvx/q7qIZ0X8GriFyXBjZy5CVWmV34dGpOX2hNIheCD167KFmwqb4ROI+COOZHP2gAMblSn1QDpSVKEE8qpqfp26Q2l3cp9SSLtH9tOAgCunVaMycMyAIRnyaUa5ILi7FQDclKNMOg0cIsIqE17f9NSAJQNNPex101Mau6049ND9QCk57cvKUYd/v79GVh4dj7sTjfu+McurN4dfCCy5XgTjtR1INmgxbIF43DPvLEAgMf+c2BIy8/dbjGqVksBPcvBjzd0hCTTLwdNY/JSldVZlc1dsDpcql7nmXVSrc0104YpGeBIY3ATAlme+dxQ9rqp82RuYrWZVryos1jx34PSi/+Ns4Z7fTrqiPpOrqIo9ioo1mgEpddNIAXAA01LTSgwQyNI01/1lsTZ1yZe/OvL03C6RUwalo5x+Wn9HmfUabHqxmm4dloxXG4RP33ny6CzEa9slj4sXDNtGMwmPX508WhMKDSjpcuBX/37QFDnBIC2bgfk+CEjSmpuSrKSYdBpYHO6g97XbSDy32BsXhpyUg3ISNbDLaq7n9XeU2349HADNALwkzljVDvvUDG4CQE55RnKzE2dhZmbaPDWF1VwuUXMHJmFcflpGJmdAq1GQIfNidoofzPvtLuUJdzy6pFhQayY8tXAT5Zk0GK0J/XOqanYI09JXTNt2KDH6rQa/O6bk3HpWblwuUX87uNDAV+vurUbaw9I01o3zR4JQCpM/801k6ARgNV7TuPTw/UBnxcAmj1ZmzRPN+5ooNUIGJ2TAiA0U1PemRtBEJQtH9SsCXzGs0LqynOHYaTnd4kG0fEXjjNZyVJVcagyN112pxLUMLiJHKfLrRQSf+f84QAAg06DkdlSWvZIlNfdyFNSyQYtkg3SVGqgRcUOlxt17f0HNwCLimPV0bp2fHWqDTqNgG9MKfLrZzQaAT9fPAEaAfh4fx12nmwO6JqvbzsJtwhcUJrdK1M0pSQD379A6pvy4Af70GkL/HUvmvaV8haqbRicLjeON/YEN9L/pql6rYM1Fqw9UAdBAO68NHqyNgCDm5CQ//G0hCi4kYuJAamgONqnP+LV+sMNqGmzIjNZj8vPKVAel+e2w7GPy1AoK6VSexrvBbocvLbNClEEDFpNv3UMLCqOTe95CnjnnJWHbK/nyGDG5qfhuhklAICVaw75/fpkdbjwxvYqAD1ZG28/XTAOwzKSUN3ajafKjvh9P7LmKFsGLgtVcFPZ3AWHS0SSXqt0DlcyNyp98Przp9IKqcWTCpXfI1owuAmBrBBPS9V5TXc4XKIytUDh9bona/OtGSV9mtcB0d/rprGjb0OzQBv5ySulCjNM0JyxkkZ2jqdT8T5mbmKGyy0qRcHX+jEldaZ7542DSa/BjpMtKDvgXy+u/3xVg+ZOO4rSTZg3Ia/P91OMOjx+9TkAgBc/r8CXVa0B3VNzlC0Dl41RporUfb2Qz1eal6L825Rfm46oMC1VXt+ONXtrAAB3XRZdWRuAwU1IyMFNqKal6tp7rxiwxNBGjfHiVEuXMvd/w8zhvb431pNOj/YVU74yNyVZ0rRUtZ/BTU8xse8pKaBnA82q5u6Y2lQ0kW051oRaixXpSXpc5iPQGExBugk/uFCaRvrtR4fg9KO1gLz8+zvnj4BO6/utac5Zebjy3CK4ReCB9/cG1LIg2paBy+RM7zGVFyEo9Ta5PRkV7/2sbM6hrZha9ekxiCKwYGI+xheYh3SuUGBwEwJyzU1ziLZgOHPViYV1N2H35vYqiCJw4ZhsjDqjiK4n9dse1VOGA01L+dvrRl4GXpjRf9OujGSDkhY/wKmpmCAXEi+ZUtgrKxmI2+eUIjNZj2MNnXjXs31Df/ZUteLLU20w6DS4/rySAY996OsTkZGsx8EaC57f6P/WDNHWwE82MicZGgFotzlR367ee4ayUsqrdinfbESavJ9VY/Crs040duKfe6TM3l2XjR3ajYYIg5sQUDI3IdoZ/Mx/AGzkF14Olxtv7ZBqA74za0Sf74/KSYFGkILOBhVfrNQmPz9zvKalclONMAbQ60beFXpYP8XEMhYVx44OmxMfeTqfX9NPbxt/mE16LPW88f3hv0fQbe8/U/CKZ6uFJZOLBq3vyUk14qErJgIAnv7vEb837ozWzI1Rp8WIbOkDkprNP+XgptQrcyMIAsbkD33F1F/Wl8MtApeelYtJxelDu9EQYXATAnLBWjhqbgCumAq3/x6oQ0O7DblpRsyfmN/n+ya914tVFE9N+crcCIKgLAf3p9fN6QGWgXvz3iGcottH+2rR7XBhdE4KppZkDOlc3z1/OIozk1BnseGFz31nWRo7bPj3V1Ltxs0X9P2w4Ms104bh4rE5sDn935pBXgoebaulAO+iYnUWIbjdolfmpneh7zhlwUNwr01VzV1Kt+i75kZn1gZgcBMSWZ6twVu67CGZlmBwE1n/2CYVEl83oxj6fmoDxnhNTYVKZVMX/vjfowN+Ih6Ir+AGCGw5uFxzUzjIXjLnDJMyNywqjn7yDuDXTBsGQfBdJO4vo06L+xeeBQB4dv0xnx/43txeCbvLjXNLMjC5OMOv8wqCgMevmgSTXoPNx5rwziDTXoDXUvAoWy0FeAU3Ku0xdbqtG90OF/RaASOyencMHuqCh2c/OwanW8RFY3IwbXjmkO81VBjchID8ycDhEpXN39QkLwVPM0q9SdilOHxONHZiU3kjBAG4/rzh/R4n190cCWHm5pf/PoA//PcI3vZMkQWqycdqKSCwFVNycDP4tJSUuTnW0Kl663dSz6mWLmw53gQAuHoIU1LelkwuwtlFZrTbnErDN5nT5cZrW6UPC/5mbWTDs5Nx37xxAIDffXwYXfaBX2uV4CY1CoMblfeYks8zKielT3H2mCE08qtts+KdHVIgGY0rpLwxuAkBk16LZM/u0M0hqLuRMzelnicpa27CR27aN2dcrrKyyBfl01GIGvm53SK2V0hvQhV+1hycqaHfzI1/vW7arQ6lmL1wkOAm32xEdooBLreIQ7XR3f8nkcnLv2ePzh40YPWXRiPggUXjAQCvbT2Jyqae51XZgTrUWqzITjFg8aTCgM99y4WjMDwrGQ3tNvx9kOLimMjcqBzc+Oo9IxcYVzR2BrTaDABW76mG3eXGeSMzMWt09tBvNIQiGtxs2LABS5YsQVFREQRBwOrVqwc8/v3338f8+fORm5sLs9mM2bNn4+OPPw7PzQYoVMvBO2xOdHqmIcYowQ2npcLB5nQpWZIbfRQSe5OXXB6pD82KqaP1HUpgEchWCTKrw6U8b3KDnJaSe9yYTTqkerKI/REEQVkSzqLi6CSKolJL4c92C4G4eGwuLh6bA4dLxO/XHlYef9mz/PuGmcODWpVl0Gnw/zzTXs9tON7vxppWhwtdntfNaMzcyB9UGzvsaO0a+nuGr2XgsqJ0E1IMWjhcIk42BbZiauPRBgDAFUEEouEW0eCms7MTU6ZMwapVq/w6fsOGDZg/fz7WrFmDnTt34tJLL8WSJUuwe/fuEN9p4LJD1MhPztqkGXXIN0tvSgxuwuOjfbVo6XKgMN2ES8/KHfDY0txUCALQ2uUISb+jL070tLWXl2MHQn5e6rUCzEm9AxM5czNYr5vTA+wG7ss5nh3C2ak4Ou2pasXxxk4k6bVYFII3r/+5XMre/N+Xp7Gvug2Ha9ux9XgztBpB2b4kGF+fVIhJw9LRYXPimU/KfR7j/XxPGyQQj4RUo07ZeFaN7I28kGGMj81OBUEIqoC52+7CFxUtAICLxw38+hcNIhrcLFq0CI899hiuvvpqv45/+umn8bOf/QznnXcexo4di1//+tcYO3Ys/vWvf4X4TgPXs3mmukuB5eAmz2yE2SQVLrPmJjzkQuLrzxveb5MxWZJBixJPBkTN5Z2yHV7BTTC7CcvFxNkpxj5Fo/72upFXSvk7faEsB69m5iYayb1tLj+nYNBMXDDOGZaOq86V9qj6zYeHlKzNwrPzUThAE8jBeE97/WPbSZxs6jtN6731wlCLpEOlVKWpKVEUB8zcAD17TAXy2rStogl2lxvDMpKUzT6jWfSFsAFwu91ob29HVlZWv8fYbDbYbD0BhsUifWp0OBxwONQNCuTzORwOZHo+DTdYrKpep8bzRpabakCyXnqDbeuyq/67xALv8Q61o/Ud2F4hfcq8ZmqBX9cszU1GZXMXDte0YsZwdTt4emdu2q1ONFm6YE7S+/3zta3S8yg7Vd/nd8kwamDUaWBzulHZ2I7hntqiM8e7qll6Ycw3G/waj7PypPMcqm1Ht9U2aICY6ML5/LY53fjXl6cBAN+Y7N/zOxh3XzYa/9lbg03ljdAdl4KMG88rHvL1Zo5Ix8VjsrGxvAlPfHQIT183udf36y3S8z0zue/zXRbO8fZldE4yNh4FDtdahnQPDe02tHU7oBGAkgzf/zZH50jBZCDX+szTkf3C0iw4nUOfLQhmvAM5NqaDm9///vfo6OjAdddd1+8xK1euxIoVK/o8vnbtWiQn918QOhRlZWVordMA0GDnvsNY03FQtXNvOC0A0MLR3oTyg40AtDh5uh5r1qxR7RqxpqysLOTXWHtKGvcJ6S7s2vSJXz+jaZeeA+u+OIDMpn2q3UuLDahu1UGACIMWsLkEvPXvMgwL4MPU1nrp93F3tfl87qTrtKh3Cnj/488wLr13zZA83jvKpd/PUnsSa9acGPSabhEwarWwOd146YOPUBSaf35xJxzP7231Atq6tUg3iGg9vA1rAt+X0m8X5GnwWY0GTreIwiQRjQe2Yo0KL5HnJwOboMV/9tZiPE5huFfSYkeD5/nebRn0tTIc4+2LtU66x60HKrBGPBb0eY62SefJMoj4pMx3TWpri3TM7mM1WLNm8GX0APDhHi0AAcntlViz5mTQ93emQMa7q8v/LHXMBjevv/46VqxYgX/+85/Iy+t/75Ply5dj2bJlytcWiwUlJSVYsGABzGZ1P007HA6UlZVh/vz5qNpyCp/WHEVG/jAsXjxJtWvs+fAwcPIkpo4fjdmjs/Dy0V3QJ5uxePFs1a4RK7zHW6/3P2sRjI0f7AeqqnHZ1LFYfGmpXz9j3V2Nde/vhyM5G4sXn6favfz7qxpg115lefW+0xaMPmcG5gawB1DVhgrg2FGMH1WMxYvP6fP9dxt2or68CcPGTcbi6VJx6Znj/foLXwANLbh05rlYPMW/Go1/1GzHjpOtyBlzLhZ7pijIt3A9vyubu/CLv2wF4MQPLxmLr39tdMiuBQDnd9ox9w+b0GFz4o75E3HFINstBOKIsBf//LIGmzvz8ONvTVemoOq3nATKD2Ps8EIsXjzF58+G8/XEl9wTLXjr+BewIBmLF18S9Hle21YJHDiEySPzsHjxVJ/HnNPchb8d2oQGuxYLL18AbT+b3spqLVbUbtkAQQDuvHYeMpKHPj7BjLc88+KPmAxu3nzzTdx666145513MG/evAGPNRqNMBr7tvPW6/UhewLr9XrkmaW0X0u3U9XrNHiWlhdmJCMjRSpA67Cre41YE8q/pazaU18yMifV72uNL8wAAJQ3dKp6f7tPSf/AzxuVhdo2K/adtqCm3R7QNZq7pLRyntnk8+dKslOA8ibUWmx9vi+Pd02bzXOs/2NyzrAM7DjZikN16o5JPAvl89vudOO+d/aiw+bEjBGZ+MmlY0M+XZifocez352O7SeaccOskf02wgzG/1s4Hh/uq8OW483YcqINX/MUvlqs0kqp7FTfz3dv4Xg98WV8UQYA6bXG7haQEmTdU0WTVOg/tiCt399jZK4ZJr0GVocbte0OjBykhmbbCWln98nFGchNVzflGsh4B/J3iblJ7zfeeAO33HIL3njjDVxxxRWRvp1+ZYVotZTcwC/fbES6p66Hq6VCT96KYKDeNmca47W8U83nwRcnpBUL543MUop5/d3FW9bkKXTPSfG9j89gjfzcblHZe6pogE0zzyQXFe9L8KLijUcb8O+vTmN3ZQvq260R22D1iY8O4atTbUhP0uOPN0wNWx3URWNzsGz+OFUDG0D69/m92VKbht98eAhutzSu8orFaNx6QZaVYlBW2R5vCK53FdBTJCy3o/BFqxGUPaf82SJGXgJ+ydicoO8r3CKaueno6EB5ec/SvYqKCuzZswdZWVkYPnw4li9fjurqarzyyisApKmom2++GX/84x8xa9Ys1NZKm7slJSUhPT26Nu/KDNHmmXXt0htKvtmENM9qqXarE6IoRu0qgFjndLmVni7yCih/pBh1GJaRhOrWbpTXd2DmqP4L3/1lsTpwqFbK3MwYkansEB9orxtl64U03y/2g/W6aey0we5yQyNIz0V/KXtM1VgS9jn71alWfO/v23s9ZtBpMCwjCcWZSRiWIf1XYDbAMvjepUH75FAdnt8kNb77/bemqNa0L9KWXjoGb++owsEaC1bvqcY104qVHcGjObgBpBVTTRXNKG9oD3pDSnkLB18N/LyNzUvF/tMWHK1v97lHnsztFrHpaCMA4KIxsRPcRDRzs2PHDkydOhVTp0rzgsuWLcPUqVPx8MMPAwBqampQWVmpHP/Xv/4VTqcTd955JwoLC5X/7rnnnojc/0BC0edGFEVlKXh+mglpJik2dblFpUEVqa+mzQqXW4RBp0Fe2sA7Fp9prAo78HrbdbIFogiMyE5GntmEYZ4gJNBeN43t8o7gvn+fkkG6FMvLwPPSTAF9+h6bnwqDVoN2qxNVzYH354kHnx6SPgVnpxhQmG6CIEjTQxWNndh4tBFvflGFJ8uO4P739uGJL7Uh2Vm+pq0bP337SwDA9y8YOeCbW6zJTDHgjjlSXdyTa4/A6nD1dCeO8uBmqJ2K27ocyvNl0OAm37/l4AdqLGjqtCPFoMXUKN5L6kwRzdzMmTNnwHTsSy+91Ovr9evXh/aGVCR3wex2uNBtdyHJEHj3zTNZrE5YHVLfkTyzEUadBlqNAJdbRLvVGfQcLQ2ssll6gy/OTIJmkMK7M43NS8X6ww2q9brZ4ZmSmjEiS7knIPBeN/K0VHa/01JS0FRrscLudMOg6x3A1CgN/PzP2gCAXqvBuIJU7Ku2YP/pNgzPTrwlU58fkz4FL1swDt+ZNQJ2pxu1bVacau1CdUs3qlu7Ud3SjU3ljahps+Lvn5/AQ0v6Fn0Hy+ly454396Cly4FzhpmxfPF41c4dLW65YBRe2XwS1a3deG3rydgJbuSpoiBfL8obpA9RhemmQXsV+bvH1EZP1mZ2aXaf14FoFjt3GmPSjDrotdIbYbMK7bQBKFMQZpMOJr0WgiAo2RvuLxU6VZ7gJpApKZk8763WnjFyf5vzRkqfoIZ5gpuWLgc6/dyk1eUWlRf7/qalclINMOo0cItQamu8VQfYndjbOUWJ26m42+7C7kopQL2wVErxG3QaDM9OxgWlOfjWjBLcO28cfvetKfjVNyYAAF7fXqVMI6rhT5+UY3tFM1KNOvz5hmlBbXsQ7ZIMWtw3fywA4JlPylHree2M9uBG2ZMuyN3B5aBosKwN0LO5b3l9h1Kb5Itcb3Px2OjvSuyNwU2ICILQU1SsUt1NfbtcTNzzabmnSzGLikOlp5g48DfyMSpOS9mdbuypagUAzBgpZW7MJj3MngDX36mp5k473CIgCP1vIigIwoBZIXlaKpjgZkKhVFR8sCbxgpsvTjTD4RIxLCMJIwbJWl0yNgfDU0R0O9z428bjqlx/c3mjsjP341efM+gqmVh27bRijM1LRVu3Q1l0Ee3BjRyUnGzqgt0Z2KaWwMAbZp5peFYyDFppxVR/rx1ddqeSLb44hoqJAQY3IZXlSfk3qbQFg1Jv4xXcyJkbbsEQOnJtSDCZG/lFps4idQ0din2n22BzupGZrEdpbs+bklJ342dRsfx8zEw2DLg6ZqCi4po2T+YmPbBpKQDKBpqJGNzIU1KzS7MHLaYWBAELS6Q3uFe3nBxy/V5jhw33vLUHogh8e0YJrjxX3c0xo41Oq8HPLu895ZYZhTuCeyswS9NJLrfocxuJwcgrnwZaKSXTaTUY7Xkd6e/D17aKZmXLhVExFggzuAkhtYuK6zzLwPPMPXUSPdNSzNyESjDLwGVmkx4FZnlDvKFlb+T9pGaMzOr1xhho3U1PMfHAL/QDZ26Cn5YaXyC98J5us6qyA3Is2XKsCQBw4Zhsv44/O0PE2UVp6LK78PwQsjdut4hlb3+JhnYbxual4tFvnB30uWLJvAl5yhRumlEX9TUjgiAoH1z8WaJ9pkAyN97H9Vfjs/GIFIxfMi4n5lY2RvdfOsZlqh7c+MrcyMvBmbkJlaFkbgCvFVNDLCru6W/Te8WCvIT3lJ/TUsoy8H5WSskGytxUD2FaKs2kV/arOpBA2Zu2Lgf2evr7XFDqX4pfEIClnpU/L28+EXQw+NeNx7HhSAOMOg3+fOM0VRY4xAJBELB88QTotYIyHRrtxgRZp9dldyrTS/4GN+PkFVP9XCtW620ABjchpXbmpl7ucZPGzE24dNtdSjAQTM0N0JMiDuaTmEwUxV6ZG2+DNdw7k7Ij+KDBje/z2pxu5RzBBDcAMKFQGpMDCVRUvOV4E0QRKM1NCag30NzxuZhQaEan3YW/e/rSBGJXZQt+//FhAMCj3zgbZxUMPmURT6YNz0TZfV/D326aEelb8Uuwy8GP1UvTWNkpBr9ri8bm9d/Ir6atG0frO6ARgAtK/cs0RhMGNyGkdpfiOkv/BcXM3ISGPCWVZtIhPYBdt7319LoJPrg51tCJli4HjDqNstpIJgch/tbcNHYMbVpKXnli0muQGeQeMxMLpd/hYI06/X9iwRZPvc2FATZCEwQB98wdAwB46fMTaOvy/9/66dZu/PjVnXC6RXx9ciGuV3Efp1gyMicF6SrshxQOYwYIOAYiLwMv9TNrA3itzqpr79OWRW7cN7k4AxlRXqvkC4ObEJKDmyaVp6XyegU3noLibmZuQsF7GXiwc87Kksu64N/I5azNuSUZfeoGBusmfKZAp6XkXjeyGnlKKj0p6DFRMjcJNC31uafeJphPwQsmFmB8QRrabU688Ll/2ZtOmxO3vrwDDe02nJWfhpXXTIq5uolEJL9eHG/ogGuAJdpn6tl2wf/gZkR2CnQaAZ12l9KFXSb3t4mlLRe8MbgJITWnpURRVPaVyus1LcXMTSgpwU2QU1JAzyex023WoP9O3vtJnUmuuWnssMHqGLxTdZMS3Az8aay/Xjc1bcHX28jkFVPl9e1BLXmNNXUWK8rrOyAIwPmjAw9uNBoBd8+V+ra88HnFoCvv3G4R9721BwdqLMhJNeD5m2corxUU3UqykmHQaWBzugPaMy7QYmJAaqopr4LyzhS53SI2lUvBzcXjYq/eBmBwE1JqTku1dTtgd/V0J5ax5ia0qlqGVkwMABnJBuR6AtJjQW6It+OkXG/Tt/15RrIeKZ4CUX963fRMSw2cuemv183pIDbMPNOwjCSYTTo4XKJqDQ6jmbxK6pyi9KBT/JefXYBx+alotzrx8uYTAx77xMeHsfZAHQw6DZ773oygVvpRZGg1AkZ7Ag55qskf5QEsA/fWs+Ch51oHaixo7rQj1ajDuSUZAZ0vWjC4CSFlWkqF7qJyvU1msr5XR1HvzTNJfT2Zm6G9OSiFe0FMTdVbrDjZ1AVBAKaN6BvcCIKgdCr255Oev9NSQM/UVJVXcCP3uClMDz5zIwgCxidQM7/PPZ+Ch1KYqdEIuOsyKXvz900V/WYB39lRhWc/OwYAeOLayZju4zlD0a10kCXaZ7I5XTjpea0KJHMjHd93ddYGzyqp80dnq75ze7jE5l3HCDm4sVidcLiGlnr3tQwcYBO/UFMyN0OYlgIGXpUwmB0npSmp8QVmpYD8TP7W3YiiqOxUnz3ItJR03r4rpuRpqaHuIj3RE9zEe92NKIrYLNfbDHFX5cWTClGam4K2bgde2XKyz/e3HW/Czz/YCwC467IxuGpqfDfqi1djA1wxdaKxCy63iDSjDvnmwT+0+LqW92uTd3+bWMXgJoQykg2Q6/dahtiszFcxMQCYk5i5CRVRFHFqCPtKeevZgTfwzM2Z+0n5Igca1a0DN/KzWJ3K9GYgmRvv4GYoWy94m5ggmZvK5i5Ut3ZDrxUG/Bv6Q+tVe/O3jcfR4bWf2MmmTtz+2k44XCKumFSI++aNG9K1KHKU5eB+7jElB0GleakBF417T0uJoihtueCZBo/F/jYyBjchpNUISrvvodbdKPtKpfV+Q2LmJnRauxxo97x5FA81uBlK5kbeCdxHMbHM31438pRUmlHafHUwZ9bciGJP5qZwCDU3QE9R8YEaS59lqPHk83IpazO1JBPJhoF3avbH1ycXYXROClq7HHhlywkAUk3eD1/egZYuByYXp+P335oS8A72FD28e934829D3j4hkJVSslE5KdAI0gefhnYbth2X9j8rzkzCyEH2P4tmDG5CTK3NMwebluqwOQfc2ZUCJ9eZ5KYZh9zRVc7cnGrpRpfd/yxbh82J/aelrrYDZm78rLkJZEoK6Kk1koOmbhfQaZdWZBUNoeYGkF7AtRoBrV0OpXdOPNrs6W9zgZ9bLgxGqxGw9DKp783zGytgsTqw9PVdKK/vQIHZhOdvmpEwHYjjlRxwtFudygfbgQSzUkpm1GkxMrtnxdQGr67Esdw6gMFNiKnV66YnuOmduZFrMEQR6AzgTZMG17PtwtDexAHpeSC3BpA7ifpjT2Ur3KI07TRQAa+/NTeBFBNL55WuKfe6afG8zmalGIb8BmrSazEmV3oxjtdOxW636LWflHr1C9+YUoSR2clo7rTjyj9/jo1HG5Gk1+L5m2f0mbqm2GPUaTHCE3D4U3ejrJTKDzy4AXqCoiN17THf30bG4CbE1Op1I6+Wyk3r/cJl1Gmg10rRNetu1DWUDTN96ek86n/djT/1NkBPzU1du3XAvjGBBjfZKQaY9BppOspiRYtdeq4VBrEbuC9yM794rbs5XNeOpk47kvRaTCnOUO28Oq0Gd14qZW8qGjshCMDT15+Lc4alD/KTFCtKc/0rKna63DjeKH1gGpMb3NYaclC08WgjypUtFxjc0ADU2jyzQa65OSNzIwiCkr1h3Y26qlQqJpYFsw1DT3+b/uttgJ6Ge1JNTP/Zm8YAp6WkXjfS71/d0o1WT+ZmqMXEMu+6m3gkr5KaOSpL9R2pr546TKmJ+NnC8Vh4doGq56fI8vfDUFVLN+xON0x6jTI9HSi5N84nh+oBAFNKMmJmu4r+MLgJMTUyN2632LNppo+UMxv5hYZay8BlygaafvaucLjc2F3ZCsB3Z2Jv/va6CTRzA3jtXdXajRablLkZ6jJw2QRlxVR87jG1WYX+Nv3RaTV49Yez8PIPZuL2r41W/fwUWXJx8J6q1gF3g5czO6NzpBq2YJxZqxPLq6RkDG5CTI0uxS1ddjhcUrFwblrfNyVuwRAaai0Dl/Vkbvx7Iz9YY0GX3QWzSefXKgh/6m4aPRnAHB/Po/7PK6+YsqLF8zRWb1pKCm5ONHWi0xZfwbnT5ca2Cinzpma9jbeSrGR8bVxsF36Sb+M9U7b7qi2Y+fg63PHaTpQdqOsz7Sy/ngRTTCwrzU2F91Mo1uttAAY3IddTUBx8l2K53iYn1eCzWyQzN+pzu0UlSFCr5kbO3FQ2d/m1B9QXXkvA/VnWK2dTTg2wBYNc2J6T4v8WAMq0VGs3Wj2ZG7WmpXJSjchLM0IUgUO18ZW9+aq6DR02J9KT9EpPHyJ/TSw047GrzsGEQjPsLjc+3FeLH72yA+evXIdH/28/vjrVClEUvbZdCD64STJolQ9xaUYdpsTolgveht50gQaUnSJ9Qh5K5qbOMyWVl+b703JPrxsGN2qpa7fC7nJDqxFUy1LkpBqQkaxHa5cDxxo6cHbRwMWf8k7gvvaT8sXXPlBnUqalgsjcVLd2K5kbtYIbQKq7qT/cgAM1lrjaKkCekpo9Ops9ZyhggiDgu+ePwHfPH4EDpy34YPcprN5zGg3tNry0+QRe2nwCY/NSlU1Uh5K5AaTgqLK5C+eXxu6WC95i/zeIcmpMS9X3swxcphQUD7JTMPlPXgZelGGCTqV/6IIg+N1WXRTFAXcC96XYn5qb9mBqbqRPdJXN3gXF6i03nhCnnYrl5n0XqtTfhhLXxCIzfnHFRGx54DK8dMt5WDKlCEadBkfrO5Q+OMEuA5ddfk4BBAG4YWaJGrcccczchJgc3LR0OeB2i0F9gpOnpXwVEwPcPDMU1F4pJRuTl4YvTrQMWlR8sqkLjR02GLQaTPJzee9gXYq77S6lAZ+/q6W8zyu9iArQaoR+s4jBUPaYiqNeN1aHCzsrpeB0qPtJEcl0Wg3mnJWHOWflwWJ14MO9Nfi/L08j32zC6JyhBTffmlGCa6cVx02WkcFNiGWmSIGHyy3CYnUgI9n/NxWZsq9UP1MJPTU3zNyoRelxo3JwM9bP5Z1yf5vJxel+bZMAAMMypHuttVjhdLn7ZJzkKSmDToM0o///9OVeN1aHVMhYYDYGvSrDFzlzc7i2HS63qOq5I2XnyRbYnW7km40YnZMS6duhOGQ26fHt84bj2+cNV+2c8RLYAAxuQs6o0yLNqEO7zYmmTntQwY2cduyv8ygLitWndCdWaRm4TE4df3WqDe/sqAIAZaWL/LIiCMD/fXkawOD9bbzlpRmh1wpwuETUWqx99sOSg5vcVGNAq2vkXjfyVJpaNUiyUTkpMOk16Ha4cKKpU2leFsvkLRcuLM3hSiaiCGBwEwZZqQa025xo7rSjNIj2AfX97CslM3MpuOrU7k4sG+fZY6qmzYr73/1q0OMD2UVaoxFQlJGEk01dqG7p7hPcBLqvlLfizKSQBTdajYCzCsz4sqoVB2ssIQluRFEMa5Ah19twSoooMhjchEFWigEnm7qUN5dA9dTc9FNQnMTVUmpTetyoHNzkm034n8vHY3uF9OYnb3Uqb/zrvfVpcWYSLhkXWDRcnCkFN6daujHrjO8F08DP+7yyoW6Y6cvEQim4OXDagq9PLlLtvC63iOv/ugVWhxuv/nBmUJnTQFmsDnx1qhVAaJr3EdHgGNyEwVC6FLvcIho6/C0ojmzm5qXPK/Dbjw5jdmk2rptRgrkT8mJySaHN6UKNJ1umds0NANwxpxR3zClV/bxAT6+bah+9buTgJjuAHjcy7yxQYXrgwdFgJoZoj6kdJ5qVVWd3vr4LL90yM+TPye3Hm+EWpek2NZfME5H/GNyEQWayHNwE3sivqdMGl1uERuj/TSlaam4+3l+HbocLnxyqxyeH6pGTasA104px3YySIfdgCKfTrVaIIpCk1yIniCmcSOrpUty31428r1QgPW56ztvzJl0YgjfsUO0x9dH+WuX/f17ehF/9+wB+eeU5QZ1r58lmvLG9ClNKMjBnXG6/Wb3Pj4VuywUi8g+DmzDISpWDm8AzK/WWnqmE/vqtRMtScHlV1+VnF2DHyRY0dtjw1w3H8dcNxzF9RCaum1GMKyYXITWAlTqRIC8DL85MirliUO+Ge2cayrSUdwarSOWaGwA4q0AKbuosNjR12JAdxD2eSRRFrN1fBwC4/rwSvPlFFV7ZchLj8tPw3fNHBHSu9Yfr8eNXd8LmdOPdnacAAKW5KfjauDzMOSsXM0dlKavaNsv1NjG+qzJRLIvud5k40TMtFXjmRlkG3k+9DdCTuemwOSO2lFYUpRU6APCzy89CSVYyPj1Uj7d3nMKnh+ux82QLdp5swYp/HcDXJxfipwvO6neaLdJCVUwcDsoWDD563fQEN8EVFMtCEdykGnUYmZ2ME01dOFjTjovGDj242X/agurWbiTptXj0G2ejJCsZv/v4MB79v/0ozU3FbD8zKx/tq8Vdb+yCwyVipmf12s7KFhxr6MSxhgq88HkFkvRazC7NxqxRWThcJy3z9/f8RKQ+BjdhkOXZgqEpiJobpZh4gKZpcnADAB1WZ0S2qu+wOdHlaRBXkG6CXqvBgrMLsODsAtRbrHhvVzXe2VGF442deHvHKRh0Gjx21aSw36c/lGXgmbFXL1HsCchOt3b3aRopF7QHk7nJTjXirktH41j5UZiTQvP8mlBo9gQ3FlykwsZ9H3umpL42LhcmvRY/mVOKw7Xt+L8vT+OOf+zE/915EYZnDxzArt5djZ++8yVcbhFXTC7E098+F3qtBm3dDnxe3oj1h+vx2ZEG1FlsynSs/LtkBVHbRETqiL1qzxg0lILienlfqQGyHEadFkad9Ke0RKioWM4wpZl0SDb0jpnzzCbcMacU6376Ndy/8CwAPQFENIrlzE1+mtRgz+ESlf5IsqFMSwHA3ZeNwcJicfADg6R0Klap7uajfVJwc/k5BQCkfj1PfHMyphSno7XLgR++/MWARfhvbK/EfW/vgcst4pvTi/Gn66cqxcjpSXosnlSIJ745BVuXz8WH91yM/7l8PGaNykKqUYfvnq9eYzUiChwzN2EwlP2lBlsGLksz6WHrsEWs7qa2TbrPggGCMEEQcI5nKwE5GIpGoVoGHg46rQaF6SacaulGdWsXCjxTSA6XGy1d0ht5MH1uwkHNPaaON3TgaH0HdBoBl47PUx436bX4600z8I0/b8LR+g7c8+Ye/O2mGX2mcv++qQK/+vcBAMD3zh+BFd84u9/urYIgYEKhGRMKzSFbBUdEgQkqc/Pyyy/jP//5j/L1z372M2RkZOCCCy7AyZMnVbu5eCEHN02ddohiYJ98B2vgJzNHeAsGud6mYJB6DDn4iebgpjJE+0qFi6+6mxZPYK0RelbvRRt5xVR5fQdsTteQzvWxp5B4dmk20s+YRss3m/DX782AUafBJ4fq8cTHh3p9/8+fHFUCmx9fMhq/vLL/wIaIolNQwc2vf/1rJCVJL6BbtmzBqlWr8MQTTyAnJwf33XefqjcYD+Tgxu50K3Up/qprH3hHcFmkl4PX+RmEycFNS5cDVsfQ3sBCocPmVDIcam+9EC49y8F7ghu5V1JWirr7QqmpMN2E9CQ9nG5x0I1FByPX2yw8u8Dn96eUZOCJb04GADz32XG8t/MURFHEEx8dwu/XHgEA3DdvHB5YND7mVswRUZDTUlVVVRgzZgwAYPXq1bj22mtx22234cILL8ScOXPUvL+4kGyQamJsTjeaO+1ICWAptDwtNdguzHKRZ6RqbmrbPJmbwTJMSTplLOottkELOsNNXgaekaxXltjHmmE+dgdXetxE6ZQUIE3vTCw0Y8vxJhyssShTmIGqbbNiT1UrBAFYMDG/3+OuPHcYjtZ14M+flmP5+3vxyaF6/GdvDQDgF4sn4EeXjA7q+kQUeUFlblJTU9HUJPVyWLt2LebPnw8AMJlM6O6O3kLRSBEEQSkqDmTFlNPlVopAB8uIRE/mZuAMkyAIytRVbRROTVXF+JQU4LvXTdMQi4nDZYIKRcVrD0hZm2nDMwcsxAeAZfPHYcHEfNhdbvxnbw0EAXj86nMY2BDFuKCCm/nz5+PWW2/FrbfeiiNHjmDx4sUAgP3792PkyJFq3l/c6Gnk53+vm8YOO0RR2lhwsJb5acbIbsHg77SU9zFRGdy0hGY38HAqVmpueroUD6XHTTjJdTdDKSrumZLqP2sj02gE/OHb52LSsHToNAKe/NYUfGdWYA3+iCj6BBXcrFq1CrNnz0ZDQwPee+89ZGdLzap27tyJG264we/zbNiwAUuWLEFRUREEQcDq1asHPL6mpgY33ngjxo0bB41Gg3vvvTeY248IpddNAJtnygFDbqpx0ILGSGdu/C0oBnqmruqjMbiJi8yNdO/VLd1KAXvPjuDRnrmR9pg6cNoScPE9IBVObz3eDKD/epszpRh1+OAnF2D7L+bhmmnFAV+TiKJPUDU3GRkZ+POf/9zn8RUrVgR0ns7OTkyZMgU/+MEPcM011wx6vM1mQ25uLh588EH84Q9/COhakRZMrxu5T8lgUz1AzxYMkdgZ3Olyo6F98KXgMvn3ket0osmpGO5xIytIN0EQAJvTjcYOO3LTjEpBcbRPS43NS4NeK8BideJ0m1VZ+eWvdYfq4XKLGF+QhhHZKX7/nE6rYdM9ojgSVObmo48+wqZNm5SvV61ahXPPPRc33ngjWlpa/D7PokWL8Nhjj+Hqq6/26/iRI0fij3/8I2666SakpwdXbBgpyuaZXYFnbgarGwCkQl0gMgXFjR12uOXpMz/ePKN5WqoyhnvcyAw6jRJkynU3sVBQDEj3XporbbJ64HTgU1ODrZIiosQQVObm/vvvx29/+1sAwN69e/HTn/4Uy5Ytw6effoply5bhxRdfVPUmh8Jms8Fm66lzsVikF0yHwwGHQ91AQD6fr/NmJEmb6jW2W/2+bk2r9Eabm6of9GeS9dK0laXLrvrvNZhTzdKy3dxUA9wuJ9yDrPDOTZGyTLVt3UO614HGOxiiKCrTUoVpg495NCtKN6GmzYqTDe04uyAFjZ6WAhlJ2qB/L7XHuz8TClJxqLYd+061YM7YLL9/rsvuxIYjDQCAuWflxPTfDwjfeJOE4x1ewYx3IMcGFdxUVFRg4sSJAID33nsPX//61/HrX/8au3btUoqLo8XKlSt9TpetXbsWycmh+XReVlbW57HqOgGAFocqTmHNmkq/zrPzmAaABq01J7FmzYkBjz3SLJ2/qrYRa9asCfieh+LLJunaRrfVr2sftwCADhW1Larcq6/xDka7A+h26CBAxN6tn+FgLG9O0iU9d9Zt3Q1Uiahu1AIQcHjPF+gqH9qp1Rrv/ogt0vPp0z1HMbr7sN8/t6dJgM2pRbZRxPFdG1ERJ+1pQj3e1BvHO7wCGe+urq7BD/IIKrgxGAzKRf773//ipptuAgBkZWUpmZFosXz5cixbtkz52mKxoKSkBAsWLIDZbFb1Wg6HA2VlZZg/fz70+t49UvQH6vHW8T3QpWRi8eJZfp3v/Vd2AfWNuGj6ZCyePmzAY7OON+Pvh3dAm5SKxYsvDPp3CEbztkrgyCGMK8nH4sXnDnr8qZZu/HH/RrS7tFi0aEHQTdIGGu9g7KlqBXZsR57ZhG98/WtDPl8kHSo7ip0bKpBWMBKXXz4eP932XwAivrHwMhQGuau32uPdn4xjTVj90k60IgWLF1/s98+te2cvgBpcNWMkrrj8rJDdX7iEa7xJwvEOr2DGO5D4Iqjg5qKLLsKyZctw4YUXYvv27XjrrbcAAEeOHEFxcXStNjAajTAa+9aB6PX6kD2BfZ07L10qjGzpcvh93XpPnURRZvKgP5OZKr1hddicYf+H2dAhpQqLMpL8unZRlpQSsTvd6HQAmSlDu1+1/pY17dLvMTxr8PGOdsNzpLqVGosN3S7A6ZZWHuVnJEOv0w7p3KH8twMAk0qkqajK5m5YXfCrmaLd6cannimpRZOKYv7v5y3U4029cbzDK5DxDuTvElTi/c9//jN0Oh3effdd/O///i+GDZOyCh9++CEuv/zyYE4Z94LZPFNeKj1Yd2IAMMurpbrDv1pKLgzO9zMjYNRplfGIpqLieFgGLhvm1etG7nFjNulgHGJgEw5ZKQalIPpwbbtfP7P1eBParU7kpBoxbXhmKG+PiGJAUJmb4cOH49///nefxwNdnt3R0YHy8p4CgIqKCuzZswdZWVkYPnw4li9fjurqarzyyivKMXv27FF+tqGhAXv27IHBYFBqgKJVtqfPTYfNCZvTNeibjN3pVroZ+7cUXPpTdjtccLjc0GvDVzAir+ryZxm4LN9sQnOnHbUWq9KVNtLiYRm4TOlS3NKNhnbPSqm06F4G7m1ikRm1FisO1FgwY+TgRcUfeVZJLTg7n5tcElFwwQ0AuFwurF69GgcPHgQAnH322fjGN74Brdb/T4Y7duzApZdeqnwt18bcfPPNeOmll1BTU4PKyt7Ft1OnTlX+/86dO/H6669jxIgROHHiRLC/SlikmXTQagS43CJaOh0oSB94nORP23qt4Ncuzqmmnj9lh9WJzDD27PB3XylvBWYjDtZEVyO/qma5O3HsBzdFnsxNp92F8gZpNVtOSuwENxMK0/DJoXq8vq0Sl43PUxoT+uJ2iyg7IO0CziXgRAQEGdyUl5dj8eLFqK6uxllnSYV7K1euRElJCf7zn/+gtLTUr/PMmTNnwC6kL730Up/HgulaGg00GilIaeywoanTNmgn3zqvKSl/PonqtRok6bXodrjQHubgRt7c099pKcCr102b/9tRhJrS4yYzdrdekJn0Wql5X7sNX1a1AgBy0qK7x423b00vwevbKnGoth1LntmEP90wFRePzfV57O6qFjS025Bm0mH26Oww3ykRRaOg5i7uvvtulJaWoqqqCrt27cKuXbtQWVmJUaNG4e6771b7HuNGIF2Kld3A/ZiSkslTU+Fs5Ndhc6LDJtX5+LOvlCzaGvm53CJOt8ZP5gboqbtRgpso707sbWROCv5998WYXJyOli4Hbn5hO1Z9Wu7zw81H+6QpqcvG58Ggi+X1+0SklqBeCT777DM88cQTyMrqmQvPzs7Gb37zG3z22Weq3Vy8CaSouN7TdC3fj2JimTlJ3oIhfMGNnGFKNeqQavQ/EShnruqiJLipaeuG0y1CrxUCCtKimVx3I09LZcfQtBQgBWdv/3g2rj+vBG4R+N3Hh/HjV3f22hxWFEV8vF+akrqcU1JE5BFUcGM0GtHe3ncVQ0dHBwyG2El9h5u8M7g/m2f27LIdeOYmnJtn1rUFfp9AT31OtOwvJdfbDMtIgjZOClKHeYIbOdkRS9NSMpNei99cOxkrr5kEg1aDtQfqcOWfP8fROun151BtOyqbu2DUafC1s3xPWxFR4gkquPn617+O2267Ddu2bYMoihBFEVu3bsXtt9+Ob3zjG2rfY9wIblrK/yyC3A8knMFNILuBe5OzI3KGKtKq4millOzMItxYmpY60w0zh+Pt22ejKN2E442duHLV5/jPVzXKXlIXj81FsiHo9RFEFGeCCm7+9Kc/obS0FLNnz4bJZILJZMIFF1yAMWPG4Omnn1b5FuNHIJtn9hQUB5O5Cd+0lNLjJsCpHDnT09hhh93pVv2+AnUqDjbMPFPxGTtqR/ummYM5tyQD/7rrIlxQmo0uuwt3vr4Lf9twHABw+TmckiKiHkF91MnIyMA///lPlJeXK0vBJ0yYgDFjxqh6c/Em2/Pm0uzHtFS9vAIpgKDBHMFpqUCWgQNS/ZFBq4Hd5UZ9u3XApb7hUNXiKSaOgwZ+suLMM4Ob2M3cyLJTjXjlBzPxu7WH8dxnx9Fpd0GrETBvQl6kb42IoojfwY33/ky+fPrpp8r/f+qpp4K/ozgWSEFxXXvgGZGeLsXhz9wEOi0lCALyzEacaulGnSXywY2yDDwr9peBy4bFYXADADqtBssXTcC5xRlY/sFezB2fjww/ekERUeLwO7jZvXu3X8cFuwliIpCDm6bOgXu72JwutHZJAUq0FxTXyrVBAazqkhWYTTjV0h0VvW7iaesFWbJBh6wUA5o77TDpNUg2RP/WC4FYNKkQC84uiJsCcCJSj9/BjXdmhoIjL8UdLHMjT0kZdBqkJ/m/UZhSUGwLX+amPsjMDdDT9C/Sy8GtDhfq26Uxj6eaG0Ba/dXcaUdOqjEuP3gwsCEiX9jxKozkzE1rtwMud/+dlpUeN+bA3pDCnblxuUUlKAi05gbo6eET6eDmlKfeJsWgRWZyfO0GLNfdxMuUFBGRPxjchFGG541TFIHWAVZMKdsZBDjVI2duLGEKbpo6bHC5RWiE4FbiFKRLb7iR7lLsvQw83rIbcpfiWF8pRUQUCDaGCCO9Vppmaut24Kq/fI7zR2Vj1uhszBqVheLMJOWNVVkGHmBjPCVzE6aCYjkoyU0zQhfELuT5UdLILx6XgcumDs8EUIGJRemRvhUiorBhcBNm35xejBc/r0BVczeqmk/hnZ2nAABF6SbMGp2NmaOysK/aAiDwIl1zmDM3wewG7k3+uUhPS8XjMnDZ4kkF+PT/zcGIOAzciIj6w+AmzB76+kTcM28sdp5owbaKZmyraMLeU2043WbFB7ur8cHuauXYQBvjhbuJX0+GKbjgJl8JbmwQRTFiU0J7KlsBAMPjaBm4TBAEjMpJifRtEBGFFYObCDCb9Lh0fB4uHS81HuuyO7HrZCu2VTRhW0Uz9lS2wu5y47yRmQGfFwBsTjfsTnfId0hWetwEm7nxrJbqdrhgsToDWhmmlv2n27D9RDO0GgELuPEiEVFcYHATBZINOlw0NgcXjc0BIC1N7ra7kJkSWBFoqqnnz9ludSA7xCtk5MLnYJaBA9KmiHINUp3FGpHg5oVNJwAAi84pQFFG/GVuiIgSEVdLRSGTXhtwYANIPT9SPI3awlF3UxfkvlLeIrk7eH27Ff/68jQA4IcXjQr79YmIKDQY3MQZc5K8M3jo626GWlAMRLaR32tbK2F3uTF1eIZnVREREcUDBjdxJpyN/Hr2lQp++ivfs+t5uIMbq8OFf2w9CYBZGyKieMPgJs4oWzCEOHPTZXcqAdSQpqU8mZtwN/L7vz2n0dRpR1G6CZezkJiIKK4wuIkzcuYm1DU38pRUskGLVGPwdek9jfzCt3mmKIp44fMKAMDNF4wMqgEhERFFL76qxxllC4YQdylWVkqZTUPqTxOJRn6bjzXhUG07kg1aXH/e8LBdl4iIwoPBTZwxh6nmRo2VUt4/H87g5u+bpKzNN6cXIz3ONsokIiIGN3Gnp+YmxNNSSjHxEIMbTzFyY4cNTpd7yPc1mOMNHfjkUD0A4JYLWUhMRBSPGNzEmXBtwSDX3Aw1c5OTYoROI8AtAg0doa+7efHzEwCAuePzuC0BEVGcYnATZ8I9LVUQ4M7lZ9JoBOR5loOHupFfW5cD73o2KuXybyKi+MXgJs4oBcWhztyoNC0FhK+R3xtfVKLb4cL4gjTMLs0O6bWIiChyGNzEGXNSmDI3bUPbEdxbflrP7uCh4nC58fLmEwCAH1w0KmI7kBMRUegxuIkz4Wji53aLqG/vWQo+VOFo5PfRvlrUtFmRk2rAN6YUhew6REQUeQxu4kw4tl9o6rTD6RYhCEBu2tB3HleWg4ew5kZu2vedWSNg0mtDdh0iIoo8BjdxxnspuCiKIbmGXBuTk2qEXoXuvvLeVKHK3OyqbMHuylYYtBp89/wRIbkGERFFj+D75lNUkjM3dpcbNqc7JFkKNXYD96ZswRBAcON2i/if9/dhd7kWB/VHceHYXEwfkYlkQ9+n9Auepn1XnlukSqaJiIiiG4ObOJNq0EEQAFGUVkyFJLhRqTuxTD5PfQAFxQdrLXh/92kAAp7dUIFnN1RApxEwpSQD54/OwvmjszF9RCZauhz4cF8tADbtIyJKFAxu4oxGIyDVqEO7Vdq1Oy9N/Wv0bL2gThZEzgB12JzosDn92ohz2/FmAEBhsojzzxqG7RUtqG7txs6TLdh5sgWrPj0GvVZAbqoRLreIC0qzMbHIrMr9EhFRdGNwE4fMJr0S3ISC2tNSKUYd0ow6tNucqG2zYkxe6qA/s62iCQAwPceNJ645BzqdDqdaurHleBO2Hm/C1mNNON1mxWnPvf6AWRsiooTB4CYOhXoLhjrPMvB8FRr4yfLTTWiv70CdZfDgxu0Wsb1CytyMNUtF04IgoCQrGSVZybhuRglEUURVcze2Hm+CTitg7oQ81e6ViIiiG4ObOCQHN5bu0GRu6lTO3ADSFFd5fYdfWzAcre9AS5cDyQYtSlJ8/46CIGB4djKGZyerdo9ERBQbuBQ8DplD3MhPza0XZEqvm/bBg5utx6UpqaklGVBhJToREcUZvjXEoVA28rM6XGjrloImtVZLAT1ZIH8a+cn1NjNHZqp2fSIiih8MbuJQKLdgkKeNkvRaZQdyNfi7BYMo9tTbzBzF4IaIiPqKaHCzYcMGLFmyBEVFRRAEAatXrx70Z9avX49p06bBaDRizJgxeOmll0J+n7FGqbkJQeam1msZuJqbT/Y08hu4182xhg40dthh1GkwaVi6atcnIqL4EdHgprOzE1OmTMGqVav8Or6iogJXXHEFLr30UuzZswf33nsvbr31Vnz88cchvtPYImduLCHI3NSp3MBP1tPIb+DMzVZPf5tpwzNh1DHxSEREfUV0tdSiRYuwaNEiv49/9tlnMWrUKDz55JMAgAkTJmDTpk34wx/+gIULF4bqNmOOOSl0NTd1ISgmBnpqburbbXC5RWg1vrNCcjHx+aOzVb0+ERHFj5haCr5lyxbMmzev12MLFy7Evffe2+/P2Gw22Gw9Ux0WiwUA4HA44HCom9mQz6f2eQOVrJMCA0u3XfV7Od3SBQDITTWoeu50owCNALjcImpbO5HnYw8oURSxzRPcTB9ujprxThQc7/DieIcXxzu8ghnvQI6NqeCmtrYW+fn5vR7Lz8+HxWJBd3c3kpKS+vzMypUrsWLFij6Pr127FsnJoemBUlZWFpLz+utQiwBAi+r6ZqxZs0bVc+85ogGgQdOpY1izplzVc6fptGhzCHj/w3UY7qOPX3030NChg04QUbt/K8oOSo9HerwTDcc7vDje4cXxDq9Axrurq8vvY2MquAnG8uXLsWzZMuVri8WCkpISLFiwAGazunsNORwOlJWVYf78+dDr9aqeOxAFla147tB2CIZkLF58sarnfrl6O9DUiktnTcWicwpUPffzlVuxt9qCMZNmYJ6PjsJv7TgF7DmAqSOycOXXz4ua8U4UHO/w4niHF8c7vIIZb3nmxR8xFdwUFBSgrq6u12N1dXUwm80+szYAYDQaYTT2neLQ6/UhewKH8tz+yEyV6lfabU7V76POs5ppWFaK6ucuSE/C3moLGrt83/eOk60AgNmjs3t9P9LjnWg43uHF8Q4vjnd4BTLegfxdYmq5yezZs7Fu3bpej5WVlWH27NkRuqPo1NOh2AlRFFU7r9stor49NKulgIEb+YmiqKyUmsViYiIiGkBEg5uOjg7s2bMHe/bsASAt9d6zZw8qKysBSFNKN910k3L87bffjuPHj+NnP/sZDh06hL/85S94++23cd9990Xi9qOW3OfG5RbR7XCpdt6WLjscLilYyksLQXAzQCO/yuYu1Fqs0GsFTBvO5n1ERNS/iAY3O3bswNSpUzF16lQAwLJlyzB16lQ8/PDDAICamhol0AGAUaNG4T//+Q/KysowZcoUPPnkk3j++ee5DPwMyQatspRazeXgctCRk2qAIQQ9ZpT9pXwEN9s8WZspxRlIMmhVvzYREcWPiNbczJkzZ8BpE1/dh+fMmYPdu3eH8K5inyAISDXq0NbtQLvVodoUUqga+MnyzVJtlK+dwbd69pOaNTorJNcmIqL4EVM1N+Q/eWqqrVvFzE2bVExcEKLgpsCPzM2sUay3ISKigTG4iVPmEGyeqewrpXJ3Ypl8XovViW57T61QVXMXqlu7odUImD6C9TZERDQwBjdxSs7cqFlzI69iyg9BMTEApBl1SPbU03gXFW/z7AI+aVg6Uowx1b2AiIgigMFNnErzWg6ullplX6m+fYPUIAhCz+7gXnU38pYLrLchIiJ/MLiJU2Ylc6PetFSoC4qlcxt7XQvoydxws0wiIvIHg5s4JU9LWUIQ3Ki9I7i3M4uKa9q6UdncBY0AzGC9DRER+YHBTZxSe1rK6nChpUsKlEK1WgroKSqWp8DkVVLnDEtXficiIqKBMLiJU+YkdQuK6z17Shl1GqQnhS7IODNzs1WutxnFehsiIvIPg5s4labyUvBar3obQRBUOacvZxYUy/U27G9DRET+YnATp3pqbtTJ3CgrpUI4JQV4b8FgQ73FiorGTggCcB4zN0RE5CcGN3FKztxYutXJ3Cg9bkJYTAz0FCvXt1uxxTMlNbHQHNKpMCIiii8MbuKU2k38lJVS5tD0uJHlpRkhCIDDJeLDvbUAOCVFRESBYXATp9TefqE2DD1uAECv1SA7RQqgPjlcD4DN+4iIKDAMbuKU3MSvw+YccOd1f4Wjx41MbuRnd7oBADNHMrghIiL/MbiJU3LNjVsEOr02oQxWuDI3QO+i5fEFachMMYT8mkREFD8Y3MQpk14DnUZasj3UomJRFJU+N6HaNNObd9Ey+9sQEVGgGNzEKUEQVCsqbrc5YfNMEeWmhbagGOiduZnF/aSIiChADG7imDlJnaJiOWuTZtQhyaAd8n0NJt9rRdZMZm6IiChADG7imFqZm4Z2KbgJR9YGAEpzUwFI/W1yUsNzTSIiih+6SN8AhU6a0dPIb6iZm3apmDhcwc2MkVlYdeM0TChMC8v1iIgovjC4iWNqbcEQ7swNAFwxuTBs1yIiovjCaak4ptbmmQ0dUnCTF4aVUkREREPF4CaOmZNUqrmxhD9zQ0REFCwGN3FM/cwNgxsiIop+DG7imFml1VL1zNwQEVEMYXATx5SC4iF2KFYyNyHeEZyIiEgNDG7iWM+0VPCZG7vTjeZOOwAglz1niIgoBjC4iWOZydKGk02e4CQYTZ1S1kanEZTzERERRTMGN3Gs0LMBZW2bFaIoBnUOud4mJ9UIjWcjTiIiomjG4CaOFXiCm26HC5bu4Kam5AZ+rLchIqJYweAmjpn0WmQmS3U3NZbuoM5RL3cnZr0NERHFCAY3ca4gPQkAUNNmDernmbkhIqJYw+AmznnX3QRD2TSTmRsiIooRDG7inFx3M9TMTa6Z+0oREVFsYHAT54rk4KaVNTdERJQYGNzEObnmptbCmhsiIkoMDG7iXOEQpqVEUeyZlmLmhoiIYgSDmzhXMISCYku3E3aXGwA3zSQiotjB4CbOFXgKgTtsTrRbA9tAU14pZTbpYNJrVb83IiKiUGBwE+dSjDqYPbuDB5q96am34UopIiKKHVER3KxatQojR46EyWTCrFmzsH379n6PdTgc+OUvf4nS0lKYTCZMmTIFH330URjvNvYUBtnIjyuliIgoFkU8uHnrrbewbNkyPPLII9i1axemTJmChQsXor6+3ufxDz74IJ577jk888wzOHDgAG6//XZcffXV2L17d5jvPHYEW3fDlVJERBSLIh7cPPXUU/jRj36EW265BRMnTsSzzz6L5ORkvPDCCz6Pf/XVV/Hzn/8cixcvxujRo3HHHXdg8eLFePLJJ8N857GjKCO4FVPsTkxERLFIF8mL2+127Ny5E8uXL1ce02g0mDdvHrZs2eLzZ2w2G0ym3jUgSUlJ2LRpU7/H22w25WuLxQJAmt5yOAIrsB2MfD61zztUuakGAMDp1s6A7q3OEwxlp+ij7ncCone84xXHO7w43uHF8Q6vYMY7kGMjGtw0NjbC5XIhPz+/1+P5+fk4dOiQz59ZuHAhnnrqKVxyySUoLS3FunXr8P7778Plcvk8fuXKlVixYkWfx9euXYvk5OSh/xI+lJWVheS8wWqoFwBo8VV5FdasOen3zx08oQGgQfWxg1hjORCy+xuqaBvveMfxDi+Od3hxvMMrkPHu6ury+9iIBjfB+OMf/4gf/ehHGD9+PARBQGlpKW655ZZ+p7GWL1+OZcuWKV9bLBaUlJRgwYIFMJvNqt6bw+FAWVkZ5s+fD71er+q5hyKtvBFvHNsFl8GMxYsv8Pvnnin/HEAn5l80ExeUZofuBoMUreMdrzje4cXxDi+Od3gFM97yzIs/Ihrc5OTkQKvVoq6urtfjdXV1KCgo8Pkzubm5WL16NaxWK5qamlBUVIQHHngAo0eP9nm80WiE0di3ZkSv14fsCRzKcwejJCsVgLQFQyD31dBhBwAUZqZE1e9zpmgb73jH8Q4vjnd4cbzDK5DxDuTvEtGCYoPBgOnTp2PdunXKY263G+vWrcPs2bMH/FmTyYRhw4bB6XTivffew5VXXhnq241Z8mopi9WJTpvTr5+xOV1o65bmN/PYnZiIiGJIxFdLLVu2DH/729/w8ssv4+DBg7jjjjvQ2dmJW265BQBw00039So43rZtG95//30cP34cGzduxOWXXw63242f/exnkfoVol6aSY9Uo6eRn58baMrLwA1aDdKT+CmGiIhiR8Rrbr797W+joaEBDz/8MGpra3Huuefio48+UoqMKysrodH0xGBWqxUPPvggjh8/jtTUVCxevBivvvoqMjIyIvQbxIaCdBPK6ztQ22ZFaW7qoMcrG2amGSEIQqhvj4iISDURD24AYOnSpVi6dKnP761fv77X11/72tdw4ED0rtyJVoWe4MbfXjdyd+IcTkkREVGMifi0FIVHodKluNuv45XuxAxuiIgoxjC4SRAFAe4vVe81LUVERBRLGNwkiMIA95di5oaIiGIVg5sEIS8HP+13cOPZV4rBDRERxRgGNwki+Job0yBHEhERRRcGNwmi0CzV3LR0OWB1+N6HyxtrboiIKFYxuEkQ5iQdkvRaAIPX3bjdIho7WHNDRESxicFNghAEQZmaGmzFVGu3Aw6XCADISWVwQ0REsYXBTQKRi4prLQPX3cj1NpnJehh0fIoQEVFs4TtXAin0s9dNPVdKERFRDGNwk0D87XXDlVJERBTLGNwkkAI/a264UoqIiGIZg5sEEnjmhsENERHFHgY3CaQnczNwQTEzN0REFMsY3CQQuaC4scMOm7P/Rn7ceoGIiGIZg5sE4r20u95i6/c4Zm6IiCiWMbhJIP428mPNDRERxTIGNwmmcJC6G6vDhXarEwCQy6XgREQUgxjcJBi57qa/FVNy1sag08Bs0oXtvoiIiNTC4CbBDNbrpt5rSkoQhLDdFxERkVoY3CSYwXrdcKUUERHFOgY3CabA7MncWAaelmIxMRERxSoGNwmmp+bGd0Exl4ETEVGsY3CTYOSam/p2Gxwud5/vc9NMIiKKdQxuEkx2igF6rQBR7MnSeGPmhoiIYh2DmwSj0QjIN8tFxX2nplhzQ0REsY7BTQIq8tTd+FoOXs/VUkREFOMY3CSggn6Wg7vdIho77ABYc0NERLGLwU0C6m9/qeYuO1xuEYIAZKcaInFrREREQ8bgJgH1l7mR622ykg3Qa/nUICKi2MR3sATU3+aZXClFRETxgMFNAiroZ/PMBgY3REQUBxjcJCA5c1PXboPLLSqPc6UUERHFAwY3CSgn1QidRoDLLSrZGoDdiYmIKD4wuElAWq9Gft51N6y5ISKieMDgJkH5WjHF7sRERBQPGNwkqAIfvW5YUExERPGAwU2CKpT3l7Iwc0NERPGFwU2COjNz02V3osPmBMDMDRERxTYGNwmqUOl1IxUUy1mbJL0WqUZdxO6LiIhoqKIiuFm1ahVGjhwJk8mEWbNmYfv27QMe//TTT+Oss85CUlISSkpKcN9998Fq7bvDNfXvzMyN90opQRAidl9ERERDFfHg5q233sKyZcvwyCOPYNeuXZgyZQoWLlyI+vp6n8e//vrreOCBB/DII4/g4MGD+Pvf/4633noLP//5z8N857FNaeRnscLt1e+G9TZERBTrIh7cPPXUU/jRj36EW265BRMnTsSzzz6L5ORkvPDCCz6P37x5My688ELceOONGDlyJBYsWIAbbrhh0GwP9ZaXZoRGABwuEU2ddtRb2J2YiIjiQ0SLK+x2O3bu3Inly5crj2k0GsybNw9btmzx+TMXXHABXnvtNWzfvh0zZ87E8ePHsWbNGnzve9/zebzNZoPN1tOF12KxAAAcDgccDoeKvw2U86l93lDJTTOizmJDVVM76jy1Nzkp+pi5/1gb71jH8Q4vjnd4cbzDK5jxDuTYiAY3jY2NcLlcyM/P7/V4fn4+Dh065PNnbrzxRjQ2NuKiiy6CKIpwOp24/fbb+52WWrlyJVasWNHn8bVr1yI5OXnov4QPZWVlITmv2kxuLQAB//l0M/Y1CwA0aD59AmvWVET61gISK+MdLzje4cXxDi+Od3gFMt5dXV1+Hxtzy2LWr1+PX//61/jLX/6CWbNmoby8HPfccw9+9atf4aGHHupz/PLly7Fs2TLla4vFgpKSEixYsABms1nVe3M4HCgrK8P8+fOh1+tVPXcorGnbg5MH6jFszNkoP9IINDTiwumTsXj6sEjfml9ibbxjHcc7vDje4cXxDq9gxlueefFHRIObnJwcaLVa1NXV9Xq8rq4OBQUFPn/moYcewve+9z3ceuutAIBJkyahs7MTt912G37xi19Ao+ldRmQ0GmE09q0j0ev1IXsCh/LcairKlDJX9R0ONHTYAQCFGckxce/eYmW84wXHO7w43uHF8Q6vQMY7kL9LRAuKDQYDpk+fjnXr1imPud1urFu3DrNnz/b5M11dXX0CGK1WCwAQRTF0NxuHCpX9pbrR0MGtF4iIKD5EfFpq2bJluPnmmzFjxgzMnDkTTz/9NDo7O3HLLbcAAG666SYMGzYMK1euBAAsWbIETz31FKZOnapMSz300ENYsmSJEuSQfwo8jfyqW7vR1MGl4EREFB8iHtx8+9vfRkNDAx5++GHU1tbi3HPPxUcffaQUGVdWVvbK1Dz44IMQBAEPPvggqqurkZubiyVLluDxxx+P1K8Qs+TMzYHTFrhFQCMA2akMboiIKLZFPLgBgKVLl2Lp0qU+v7d+/fpeX+t0OjzyyCN45JFHwnBn8a3As3lmp90FAMhKMUKrYXdiIiKKbRFv4keRk282wXunBU5JERFRPGBwk8AMOg1yvKahWExMRETxgMFNgpPrbgBmboiIKD4wuElwct0NwMwNERHFBwY3CY6ZGyIiijcMbhKc3OsGAHLTTAMcSUREFBsY3CS4XpkbMzM3REQU+xjcJLgCr+Amlw38iIgoDjC4SXDemRsWFBMRUTyIig7FFDnFmcm4cEw2MpIMSDHy6UBERLGP72YJTqsR8I9bz4/0bRAREamG01JEREQUVxjcEBERUVxhcENERERxhcENERERxRUGN0RERBRXGNwQERFRXGFwQ0RERHGFwQ0RERHFFQY3REREFFcY3BAREVFcYXBDREREcYXBDREREcUVBjdEREQUVxjcEBERUVzRRfoGwk0URQCAxWJR/dwOhwNdXV2wWCzQ6/Wqn59643iHF8c7vDje4cXxDq9gxlt+35bfxweScMFNe3s7AKCkpCTCd0JERESBam9vR3p6+oDHCKI/IVAccbvdOH36NNLS0iAIgqrntlgsKCkpQVVVFcxms6rnpr443uHF8Q4vjnd4cbzDK5jxFkUR7e3tKCoqgkYzcFVNwmVuNBoNiouLQ3oNs9nMfxxhxPEOL453eHG8w4vjHV6BjvdgGRsZC4qJiIgorjC4ISIiorjC4EZFRqMRjzzyCIxGY6RvJSFwvMOL4x1eHO/w4niHV6jHO+EKiomIiCi+MXNDREREcYXBDREREcUVBjdEREQUVxjcEBERUVxhcKOSVatWYeTIkTCZTJg1axa2b98e6VuKGxs2bMCSJUtQVFQEQRCwevXqXt8XRREPP/wwCgsLkZSUhHnz5uHo0aORudkYt3LlSpx33nlIS0tDXl4errrqKhw+fLjXMVarFXfeeSeys7ORmpqKa6+9FnV1dRG649j2v//7v5g8ebLSyGz27Nn48MMPle9zrEPrN7/5DQRBwL333qs8xjFXz6OPPgpBEHr9N378eOX7oRxrBjcqeOutt7Bs2TI88sgj2LVrF6ZMmYKFCxeivr4+0rcWFzo7OzFlyhSsWrXK5/efeOIJ/OlPf8Kzzz6Lbdu2ISUlBQsXLoTVag3znca+zz77DHfeeSe2bt2KsrIyOBwOLFiwAJ2dncox9913H/71r3/hnXfewWeffYbTp0/jmmuuieBdx67i4mL85je/wc6dO7Fjxw5cdtlluPLKK7F//34AHOtQ+uKLL/Dcc89h8uTJvR7nmKvr7LPPRk1NjfLfpk2blO+FdKxFGrKZM2eKd955p/K1y+USi4qKxJUrV0bwruITAPGDDz5Qvna73WJBQYH4u9/9TnmstbVVNBqN4htvvBGBO4wv9fX1IgDxs88+E0VRGlu9Xi++8847yjEHDx4UAYhbtmyJ1G3GlczMTPH555/nWIdQe3u7OHbsWLGsrEz82te+Jt5zzz2iKPL5rbZHHnlEnDJlis/vhXqsmbkZIrvdjp07d2LevHnKYxqNBvPmzcOWLVsieGeJoaKiArW1tb3GPz09HbNmzeL4q6CtrQ0AkJWVBQDYuXMnHA5Hr/EeP348hg8fzvEeIpfLhTfffBOdnZ2YPXs2xzqE7rzzTlxxxRW9xhbg8zsUjh49iqKiIowePRrf+c53UFlZCSD0Y51wG2eqrbGxES6XC/n5+b0ez8/Px6FDhyJ0V4mjtrYWAHyOv/w9Co7b7ca9996LCy+8EOeccw4AabwNBgMyMjJ6HcvxDt7evXsxe/ZsWK1WpKam4oMPPsDEiROxZ88ejnUIvPnmm9i1axe++OKLPt/j81tds2bNwksvvYSzzjoLNTU1WLFiBS6++GLs27cv5GPN4IaIfLrzzjuxb9++XnPkpL6zzjoLe/bsQVtbG959913cfPPN+OyzzyJ9W3GpqqoK99xzD8rKymAymSJ9O3Fv0aJFyv+fPHkyZs2ahREjRuDtt99GUlJSSK/NaakhysnJgVar7VPhXVdXh4KCggjdVeKQx5jjr66lS5fi3//+Nz799FMUFxcrjxcUFMBut6O1tbXX8Rzv4BkMBowZMwbTp0/HypUrMWXKFPzxj3/kWIfAzp07UV9fj2nTpkGn00Gn0+Gzzz7Dn/70J+h0OuTn53PMQygjIwPjxo1DeXl5yJ/fDG6GyGAwYPr06Vi3bp3ymNvtxrp16zB79uwI3lliGDVqFAoKCnqNv8ViwbZt2zj+QRBFEUuXLsUHH3yATz75BKNGjer1/enTp0Ov1/ca78OHD6OyspLjrRK32w2bzcaxDoG5c+di79692LNnj/LfjBkz8J3vfEf5/xzz0Ono6MCxY8dQWFgY+uf3kEuSSXzzzTdFo9EovvTSS+KBAwfE2267TczIyBBra2sjfWtxob29Xdy9e7e4e/duEYD41FNPibt37xZPnjwpiqIo/uY3vxEzMjLEf/7zn+JXX30lXnnlleKoUaPE7u7uCN957LnjjjvE9PR0cf369WJNTY3yX1dXl3LM7bffLg4fPlz85JNPxB07doizZ88WZ8+eHcG7jl0PPPCA+Nlnn4kVFRXiV199JT7wwAOiIAji2rVrRVHkWIeD92opUeSYq+mnP/2puH79erGiokL8/PPPxXnz5ok5OTlifX29KIqhHWsGNyp55plnxOHDh4sGg0GcOXOmuHXr1kjfUtz49NNPRQB9/rv55ptFUZSWgz/00ENifn6+aDQaxblz54qHDx+O7E3HKF/jDEB88cUXlWO6u7vFn/zkJ2JmZqaYnJwsXn311WJNTU3kbjqG/eAHPxBHjBghGgwGMTc3V5w7d64S2IgixzoczgxuOObq+fa3vy0WFhaKBoNBHDZsmPjtb39bLC8vV74fyrEWRFEUh57/ISIiIooOrLkhIiKiuMLghoiIiOIKgxsiIiKKKwxuiIiIKK4wuCEiIqK4wuCGiIiI4gqDGyIiIoorDG6IiIgorjC4IaKY8/3vfx9XXXVVpG+DiKIUgxsiIiKKKwxuiChqvfvuu5g0aRKSkpKQnZ2NefPm4f7778fLL7+Mf/7znxAEAYIgYP369QCAqqoqXHfddcjIyEBWVhauvPJKnDhxQjmfnPFZsWIFcnNzYTabcfvtt8Nut0fmFySikNBF+gaIiHypqanBDTfcgCeeeAJXX3012tvbsXHjRtx0002orKyExWLBiy++CADIysqCw+HAwoULMXv2bGzcuBE6nQ6PPfYYLr/8cnz11VcwGAwAgHXr1sFkMmH9+vU4ceIEbrnlFmRnZ+Pxxx+P5K9LRCpicENEUammpgZOpxPXXHMNRowYAQCYNGkSACApKQk2mw0FBQXK8a+99hrcbjeef/55CIIAAHjxxReRkZGB9evXY8GCBQAAg8GAF154AcnJyTj77LPxy1/+Evfffz9+9atfQaNhMpsoHvBfMhFFpSlTpmDu3LmYNGkSvvWtb+Fvf/sbWlpa+j3+yy+/RHl5OdLS0pCamorU1FRkZWXBarXi2LFjvc6bnJysfD179mx0dHSgqqoqpL8PEYUPMzdEFJW0Wi3KysqwefNmrF27Fs888wx+8YtfYNu2bT6P7+jowPTp0/GPf/yjz/dyc3NDfbtEFEUY3BBR1BIEARdeeCEuvPBCPPzwwxgxYgQ++OADGAwGuFyuXsdOmzYNb731FvLy8mA2m/s955dffonu7m4kJSUBALZu3YrU1FSUlJSE9HchovDhtBQRRaVt27bh17/+NXbs2IHKykq8//77aGhowIQJEzBy5Eh89dVXOHz4MBobG+FwOPCd73wHOTk5uPLKK7Fx40ZUVFRg/fr1uPvuu3Hq1CnlvHa7HT/84Q9x4MABrFmzBo888giWLl3KehuiOMLMDRFFJbPZjA0bNuDpp5+GxWLBiBEj8OSTT2LRokWYMWMG1q9fjxkzZqCjowOffvop5syZgw0bNuB//ud/cM0116C9vR3Dhg3D3Llze2Vy5s6di7Fjx+KSSy6BzWbDDTfcgEcffTRyvygRqU4QRVGM9E0QEYXD97//fbS2tmL16tWRvhUiCiHmYYmIiCiuMLghIiKiuMJpKSIiIoorzNwQERFRXGFwQ0RERHGFwQ0RERHFFQY3REREFFcY3BAREVFcYXBDREREcYXBDREREcUVBjdEREQUVxjcEBERUVz5/9tSeWGCabWUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plt.plot(x,y)\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_loss_list)\n",
    "plt.title(\"train_loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, test_loss_list)\n",
    "plt.title(\"test_loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_accuracy_list)\n",
    "plt.title(\"train_accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, test_accuracy_list)\n",
    "plt.title(\"test_accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_loss_list, label=\"train\")\n",
    "plt.plot(epoch_step, test_loss_list, label=\"test\")\n",
    "plt.legend()\n",
    "plt.title(\"loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_accuracy_list, label=\"train\")\n",
    "plt.plot(epoch_step, test_accuracy_list, label=\"test\")\n",
    "plt.legend()\n",
    "plt.title(\"accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "databundleVersionId": 31148,
     "sourceId": 3362,
     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30699,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 857.526724,
   "end_time": "2024-05-12T10:07:11.902700",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-05-12T09:52:54.375976",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
